% -*- text -*-
% Abbreviations -- publishers
@String{LEA =    "Lawrence Erlbaum Associates"}

@String{LEAaddr = "Hillsdale, NJ"}

@InProceedings{cnlp:aleksander94,
  title =        "Research challenge for symbolic and neural
                 approaches",
  author =       "I. Aleksander",
  year =         "1994",
  number =       "38",
  pages =        "8/1--8/2",
  publisher =    "IEE",
  address =      "Stevenage, UK",
  booktitle =    "IEE Colloquium on Symbolic and Neural Cognitive
                 Engineering London, UK, Feb 14, 1994",
  abstract =     "At general conferences papers on cognitive
                 applications are in a small minority, and most of those
                 are cognitive only by virtue of the fact that some
                 world knowledge is stored as a learned function of a
                 feed-forward neural net. This paper suggests that the
                 future of cognitive modelling lies in a proper
                 exploitation of neural state machines which have the
                 power representing world knowledge and reacting
                 appropriately to natural language. Details of this
                 philosophy may be found in recently published
                 literature. Here it is possible to look very briefly at
                 some of the challenges that this work entails. 3
                 Refs.",
  keywords =     "Neural networks Cognitive systems Learning systems
                 Iconic learning",
}

@Article{cnlp:aleksander93,
  title =        "Iconic neural representations and the learning of
                 natural language",
  author =       "I. Aleksander",
  journal =      "Colloquium Digest -- IEE",
  year =         "1993",
  number =       "92",
}

@InProceedings{cnlp:allen87,
  title =        "Several Studies On Natural Language And
                 Back-propagation.",
  author =       "R. B. Allen",
  year =         "1987",
  pages =        "II/335--341",
  publisher =    "SOS Printing",
  note =         "Available from IEEE Service Cent (cat n 87TH0191-7),
                 Piscataway, NJ, USA",
  address =      "San Diego,CA",
  booktitle =    "IEEE First International Conference on Neural
                 Networks. San Diego, CA, June 1987",
  abstract =     "Recent developments in neural algorithms are described
                 that provide anapproach to natural-language processing.
                 Two sets of brief studies show how networks may be
                 developed for processing simple demonstratives and
                 analogies. Two longer studies consider pronoun
                 reference and natural-language translation. Taken
                 together, the studies provide additional support for
                 the applicability of these algorithms to natural
                 language processing. 22 refs.",
  keywords =     "Systems Science AND Cybernetics Computer Programming -
                 Algorithms Artificial Intelligence Information Science
                 - Language Translation and Linguistics Natural Language
                 Processing BACK-Propagation Neural Algorithm",
}

@InProceedings{cnlp:allen89a,
  author =       "R. B. Allen and M. E. Riecken",
  title =        "Reference in Connectionist Language Users",
  booktitle =    "Proceedings of the International Conference on
                 Connectionism in Perspective, Zurich, Switzerland,
                 11--13 Oct 1989",
  year =         "1989",
}

@InProceedings{cnlp:allen89b,
  title =        "Identifying and discriminating temporal events with
                 connectionist language users.",
  author =       "R. B. Allen and S. M. Kaufman",
  year =         "1989",
  number =       "313",
  pages =        "284--286",
  publisher =    "IEE",
  ISBN =         "0537-9987",
  address =      "Stevenage, UK",
  booktitle =    "First IEE International Conference on Artificial
                 Neural Networks, London, UK, 16--18, Oct 1989",
  abstract =     "The connectionist language user paradigm is applied to
                 several studies of the perception, processing, and
                 description of events. In one study, a network was
                 trained to discriminate the order with which objects
                 appeared in a microworld. In a second study, networks
                 described sequences of events in the microworld using
                 'verbs.' In a third study 'plan recognition' was
                 modeled. In the final study, networks answered
                 questions that used verbs of possession. These results
                 further strengthen the generality of the approach as a
                 unified model of perception, action, and language.
                 (author abstract) 14 Refs.",
  keywords =     "Systems Science AND Cybernetics - Neural Nets Computer
                 Programming Languages Automata Theory Connectionist
                 Language Temporal Events Natural Language Sequential
                 Network Architectures Learning Algorithms",
}

@Proceedings{cnlp:anon91,
  title =        "2nd International Conference on Artificial Neural
                 Networks.",
  editor =       "Anon",
  year =         "1991",
  number =       "349",
  pages =        "383p",
  publisher =    "IEE",
  address =      "Stevenage, UK",
  booktitle =    "2nd International Conference on Artificial Neural
                 Networks London, Bourenmouth, UK, November 1991",
  abstract =     "This conference proceedings contains 81 papers. The
                 main subjects are: neural networks theory, neural
                 networks architecture, neural networks implementation,
                 image processing applications, dynamical systems,
                 control and robotics applications, speech and natural
                 language processing applications, medical applications,
                 and character recognition.",
  keywords =     "Neural Networks Learning Systems Mathematical
                 Techniques - Applications Statistical Methods -
                 Applications Computer Architecture Robots, Industrial -
                 Control Systems Eirev Supervised Learning Problem
                 Feedforward Neural Networks Genetic Algorithms Motion
                 Perception Statistical Pattern Recognition",
}

@Proceedings{cnlp:anon92a,
  title =        "Proceedings Tenth National Conference on Artificial
                 Intelligence",
  editor =       "Anon",
  year =         "1992",
  pages =        "873p",
  publisher =    "AAAI",
  address =      "Menlo Park, CA",
  ISBN =         "0-262-51063-4",
  booktitle =    "Proceedings Tenth National Conference on Artificial
                 Intelligence - AAAI-92, San Jose, CA, USA, July 1992",
  abstract =     "This conference proceedings contain 132 papers. Topics
                 covered include: Explanation and tutoring; Inductive
                 learning, neural networks and hybrid learning, robotic
                 learning, and learning theory; Multi-agent
                 coordination; Natural language interpretation, and
                 parsing; Perception; Planning; Problem solving hardness
                 and easiness, real time problem solving, search and
                 expert systems; Representation and reasoning of
                 abduction and diagnosis, action and change, belief,
                 case-based reasoning, qualitative reasoning, temporal
                 reasoning, terminological reasoning, tractability, and
                 qualitative model construction; Robot navigation;
                 Scaling up; and General topics.",
  keywords =     "Artificial intelligence Mathematical techniques
                 Computer software Word processing Computer programming
                 Data processing Computer applications Computation
                 theory Algorithms Heuristic methods Computer software
                 Learning systems Expert systems EiREV Reasoning Neural
                 networks Constraint satisfaction problems Robot
                 navigation Inductive learning Natural language Problem
                 solving Representation Planning",
}

@Proceedings{cnlp:anon93a,
  title =        "Speech Processing",
  editor =       "Anon",
  year =         "1993",
  volume =       "2",
  pages =        "735p",
  publisher =    "IEEE, Piscataway, IEEE Service Center, New Jersey, USA
                 (IEEE cat n 93CH3252-4)",
  ISBN =         "0736-7791 0-7803-0946-4",
  booktitle =    "1993 IEEE International Conference on Acoustics,
                 Speech and Signal Processing Minneapolis, MN, USA Apr
                 27-30 1993 CC18798 IEEE Signal Processing Society",
  abstract =     "This conference proceedings contains 189 papers from a
                 conference on acoustics, speech and signal processing.
                 Topics discussed include spectral quantization for
                 speech coding, language modeling, speech recognition in
                 noise, spoken language systems, speech coding at rates
                 below 4 kb/s, speech synthesis, speech recognition
                 modeling, speech recognition by neural networks, search
                 and modeling techniques, enhancement of noisy speech,
                 speaker and language identification, miscellaneous
                 topics in speech coding, word spotting, speech analysis
                 and synthesis, adaptive modeling, medium rate speech
                 coding, and feature representation for recognition.",
  keywords =     "Speech processing Speech coding Natural language
                 processing systems Speech recognition Pattern
                 recognition Speech synthesis Artificial intelligence
                 Neural networks Signal filtering and prediction
                 Adaptive systems Classification (of information) Random
                 processes Algorithms Human speech processing Spectral
                 quantization Language modeling Noisy speech enhancement
                 Spoken language systems Speech recognition modeling
                 Hidden Markov models Speaker recognition Word
                 spotting",
}

@Proceedings{cnlp:anon94a,
  title =        "{IEE} Colloquium on Symbolic and Neural Cognitive
                 Engineering",
  editor =       "Anon",
  organization = "IEE",
  year =         "1994",
  number =       "38",
  publisher =    "IEE, Stevenage, UK",
  abstract =     "The proceedings contains 6 papers. Some of the
                 specific topics discussed are: planning with
                 constraints; variable binding in a neural network using
                 a distributed representation; and relational
                 neurocomputing.",
  keywords =     "Artificial intelligence Neural networks algorithms
                 cognitive systems knowledge based systems constraint
                 theory neurocomputing connectionist representations",
}

@Article{cnlp:arbitrio,
  title =        "Towards a connectionist model of Italian morphology.",
  author =       "A. Arbitrio and C. Delogu",
  journal =      "Neural Networks",
  year =         "1988",
  volume =       "1",
  number =       "1 Suppl",
  pages =        "287",
  address =      "CNR, Rome, Italy",
  booktitle =    "International Neural Network Society 1988 First Annual
                 Meeting Boston, Massachussetts",
  abstract =     "A lexical component for the morphological analysis of
                 a natural language understanding system is presented.
                 The system is implemented in Common LISP on a VAX GPX
                 II. The lexical component is made up of two
                 sub-systems: a segmentator for the segmentation of the
                 input word and a lexicon containing the relations among
                 the Italian morphemes. The lexical structure has some
                 properties of a connectionist network formed by a set
                 of nodes which are linked to each other and exchange
                 activation and inhibition messages.",
  keywords =     "Speech - Recognition Computer Programming Languages
                 Electric Networks Summary ONLY Connectionist Model
                 Italian Morphology Natural Language Lexical Component
                 Segmentator",
}

@PhdThesis{cnlp:buchheit,
  author =       "P. J. Buchheit",
  title =        "Infant: {A} Connectionist Like Knowledge Based and
                 Natural Language System",
  school =       "University of Illinois, Chicago",
  year =         "1992",
  umi_no =       "GAX 92-03364",
}

@InProceedings{cnlp:dunbar,
  author =       "G. Dunbar and M. Kempen and N. Maessen",
  title =        "Semantic Interaction - {A} Connectionist Model of
                 Lexical Combination",
  booktitle =    "Neural Computing - Research and Applications",
  publisher =    "IOP Publishing LTD",
  year =         "1993",
  editor =       "G. Orchard",
  pages =        "95--103",
  address =      "IOP Publishing LTD, Techno. House, Bristol BS1 6NX,
                 UK",
}

@Article{cnlp:feldman,
  author =       "J. A. Feldman",
  title =        "Structured Connectionist Models and
                 language-learning",
  journal =      "Artificial Intelligence Review",
  year =         "1993",
  volume =       "7",
  number =       "5",
  pages =        "301--312",
  key_words =    "Connectionist, Language Learning, Neural Network",
}

@Article{cnlp:harley,
  author =       "T. A. Harley",
  title =        "Connectionist Approaches to Language Disorders",
  journal =      "Aphasiology",
  year =         "1993",
  volume =       "7",
  number =       "3",
  pages =        "221--249",
  abstract =     "This paper reviews the impact of connectionism upon
                 our understanding of brain-damaged language
                 performance, and attempts to explain why it is of
                 importance for the neuropsychology of language.
                 Connectionism is an approach to modelling cognitive
                 processes using networks of interconnected, simple,
                 neuron-like units. Behaviour emerges as the result of
                 the interaction of these units. It has provided a new
                 way of thinking about cognitive processing, emphasizing
                 its low-level mechanisms. One supposed advantage of
                 connectionism is its biological plausibility. It is
                 possible to 'lesion' these systems by destroying some
                 of the units or the connections between them. It is
                 claimed that lesions to connectionist models of
                 particular cognitive systems result in the appropriate
                 acquired disorders. For example, lesioning a
                 connectionist model of reading and word pronunciation
                 results in surface dyslexia. Connectionist models of
                 surface and deep dyslexia, and of word substitutions in
                 aphasia, are described in detail. Some advantages and
                 disadvantages of connectionism are discussed.",
  key_words =    "Interactive Activation Model, Computational Model,
                 Acquired Dyslexia, Letter Perception, WORD Recognition,
                 Neural Model, Context, Paragrammatisms, Categories,
                 Impairment",
}

@PhdThesis{cnlp:jain92,
  author =       "A. N. Jain",
  title =        "Parsec: {A} Connectionist Learning Architecture for
                 Parsing Spoken Language",
  school =       "Carnegie Mellon University, Pittsburgh PA",
  year =         "1992",
  umi_no =       "GAX 92-38808",
}

@Article{cnlp:jain91,
  author =       "A. N. Jain",
  title =        "Parsing Complex Sentences with Structured
                 Connectionist Networks",
  journal =      "Neural Computation",
  volume =       "3",
  pages =        "110--120",
  year =         "1991",
}

@InCollection{cnlp:jain90,
  author =       "Jain A. N. and Waibel A. H.",
  editor =       "D. S. Touretzky",
  title =        "Incremental Parsing by Modular Recurrent Connectionist
                 Networks",
  booktitle =    "Advances in Neural Information Processing Systems",
  publisher =    "Morgan Kaufman",
  volume =       "2",
  pages =        "364--371",
  year =         "1990",
}

@InCollection{cnlp:jain92b,
  author =       "A. N. Jain",
  editor =       "J. E. et al. Moody",
  title =        "Generalization Performance in Parsec - {A} Structured
                 Connectionist Parsing Architecture",
  booktitle =    "Neural Information Processing Systems 4",
  publisher =    "Morgan Kaufmann",
  pages =        "209--216",
  year =         "1992",
}

@Book{cnlp:kandel,
  editor =       "A. Kandel and G. Langholz",
  title =        "Hybrid Architectures for Intelligent Systems",
  publisher =    "CRC Press Inc.",
  year =         "1992",
  ISBN =         "0-8494-4229-5",
}

@Article{cnlp:kwasny91a,
  author =       "S. C. Kwasny",
  title =        "Connectionist Natural Language Processing",
  journal =      "IEEE Expert-Intelligent Systems and Their
                 Applications",
  year =         "1991",
  volume =       "6",
  number =       "6",
  note =         "Is this correct?",
}

@InCollection{cnlp:kwasny92a,
  author =       "S. C. Kwasny and K. A. Faisil",
  editor =       "N. E. Sharkey",
  title =        "A Connectionist Deterministic Parser",
  booktitle =    "Connectionist Natural Language Processing:{ }Readings
                 from Connection Science",
  publisher =    "Intellect",
  address =      "Oxford",
  year =         "1992",
}

@PhdThesis{cnlp:maskara,
  author =       "A. K. Maskara",
  title =        "Recurrent Neural Networks and Grammatical Inference",
  school =       "Polytechnic University, Brooklyn, NY",
  year =         "1993",
  umi_no =       "GAX 93-12245",
}

@Book{cnlp:miikkulainen,
  author =       "R. Mikkulainen",
  title =        "Subsymbolic Natural Language Processing: an integrated
                 model of scripts, lexicon and memory",
  publisher =    "MIT Press",
  year =         "1993",
  ISBN =         "0-262-13290-7",
}

@Article{cnlp:ramanick,
  author =       "S. G. Ramanick and L. O. Hall",
  title =        "A Hybrid Connectionist Symbolic System",
  journal =      "Information Sciences",
  volume =       "71",
  number =       "3",
  pages =        "223--268",
  year =         "1993",
}

@Article{cnlp:selman,
  author =       "B. Selman",
  title =        "Connectionist Systems for Natural Language
                 Understanding",
  journal =      "Artificial Intelligence Review",
  year =         "1989",
  volume =       "3",
  number =       "1",
  pages =        "23--31",
}

@Article{cnlp:siegelman,
  author =       "H. T. Siegelman and E. D. Sontag and C. L. Giles",
  title =        "The Complexity of Language Recognition By Neural
                 Networks",
  journal =      "IFIP Transactions A - Computer Science and
                 Technology",
  year =         "1992",
  volume =       "12",
  pages =        "329--335",
  abstract =     "Neural networks have been proposed as models of
                 language acceptors. This research represents an attempt
                 to measure the ''neural complexity'' of languages, that
                 is, to quantify the difficulty of acceptance of a given
                 language by a neural network. We provide a technique
                 for estimating the number of neurons necessary to
                 recognize a regular language using a second order
                 neural network with various types of activation
                 functions. We look at several different second order
                 neural network models-with sigmoid, linear, threshold,
                 or saturated functions-and sketch relationships between
                 the number of neurons required in each of the models.
                 We see that, roughly, the number in the linear
                 activation model is an upper bound for the saturated
                 and sigmoid models, and is a far better predictor than
                 the minimal automaton size that had been used as an
                 upper bound so far. Moreover, this bound is easy to
                 compute, using techniques from the theory of rational
                 power series in noncommuting variables.",
  key_words =    "Formal Languages, Learning, Distributed Artificial
                 Intelligence",
}

@Article{cnlp:smolensky93,
  author =       "P. Smolensky and G. Legendre and Y. Miyata",
  title =        "Integrating Connectionist and Symbolic Computation For
                 The Theory Of Language",
  journal =      "Current Science",
  year =         "1993",
  volume =       "64",
  number =       "6",
  pages =        "381--391",
  abstract =     "In this article we present some of the fundamental
                 principles of a research program-the Sub-Symbolic
                 Paradigm (SSP)-based on a particular approach to
                 unifying connectionist and symbolic computation. SSP
                 has been developed primarily for the study of higher
                 cognitive domains, and in this article we focus on SSP
                 research on language and grammar. The SSP principles
                 integrating connectionist and symbolic computation are
                 developed by establishing mathematical relationships
                 between two levels of description of a single
                 computational system: at the lower level, the system is
                 formally described in terms of highly distributed
                 patterns of activity over connectionist units, and the
                 dynamics of these units; at the higher level, the same
                 system is formally described in terms of symbol
                 structures, the constraints governing them, and the
                 processes manipulating them. Applied to natural
                 language, these computational principles entail that a
                 central organizing principle of grammar is optimality:
                 a grammar is a means of determining which of any set of
                 structural analyses of an input is the most
                 well-formed. Such a Harmonic Grammar consists of a set
                 of conflicting 'soft' rules or constraints, each of
                 which is in principle violable in the appropriate
                 context. This constitutes a novel framework for formal
                 grammar which emerges from the connectionist
                 computational substrate. We describe how such soft
                 rules allow for precise treatment of a complex set of
                 interactions of semantic and syntactic constraints in a
                 single language, and of universal patterns of
                 interaction among phonological constraints.",
  key_words =    "Simple Recurrent Networks, Distributed
                 Representations, Neural Networks, Systems",
}

@InProceedings{cnlp:strain,
  author =       "E. P. Strain and R. I. D. Cowie",
  title =        "Why Connectionist Networks Provide Natural Models of
                 the Way Sentence Context Affects Identification of a
                 Word",
  booktitle =    "Neural Computing - Research and Applications",
  publisher =    "IOP Publishing LTD",
  year =         "1993",
  editor =       "G. Orchard",
  pages =        "85--93",
  address =      "IOP Publishing LTD, Techno. House, Bristol BS1 6NX,
                 UK",
  ISBN =         "0-7503-0259-3",
}

@Article{cnlp:sun93a,
  author =       "R. Sun",
  title =        "Beyond Associative Memory: logic and variables in
                 connectionist models",
  journal =      "Information Sciences",
  volume =       "70",
  number =       "1--2",
  pages =        "49--73",
  year =         "1993",
}

@Article{cnlp:touretzky,
  author =       "D. S. Touretzky",
  title =        "Special Issue on Connectionist Approaches to
                 Language-Learning - Introduction",
  journal =      "Machine Learning",
  year =         "1991",
  volume =       "7",
  number =       "2--3",
  pages =        "105--107",
}

@InProceedings{cnlp:touretzky86,
  author =       "D. S. Touretzky",
  title =        "Boltz{CONS}: Reconciling connectionism with the
                 recursive nature of stacks and trees",
  booktitle =    "Proceedings of the 8th Annual Conference of the
                 Cognitive Science Society, Amherst, MA, August 1986",
  publisher =    "[N.P.]",
  pages =        "522--530",
  year =         "1986",
}

@Book{cnlp:watson,
  author =       "M. Watson",
  title =        "Common Lisp Modules: Artificial Intelligence in the
                 era of Neural Networks and Chaos Theory",
  publisher =    "Springer-Verlag",
  address =      "New York, NY",
  year =         "1991",
  ISBN =         "0-387-97614-0",
}

@InProceedings{cnlp:small,
  author =       "S. L. Small and G. Cottrell and L. Shastri",
  title =        "Towards Connectionist Parsing",
  booktitle =    "Proceedings of the National Conference on Artificial
                 Intelligence: AAAI",
  year =         "1982",
}

@InCollection{cnlp:hinton,
  author =       "G. E. Hinton",
  editor =       "G. E. Hinton and J. A. Anderson",
  title =        "Implementing Semantic Networks in Parallel Hardware",
  booktitle =    "Parallel Models of Associative Memory",
  publisher =    LEA,
  address =      LEAaddr,
  year =         "1981",
}

@TechReport{cnlp:hinton84,
  title =        "Distributed Representations",
  author =       "G. E. Hinton",
  institution =  "Carnegie Mellon University, Computer Science
                 Department",
  address =      "Pittsburgh, PA",
  number =       "CMU-CS-84-157",
  year =         "1984",
}

@InProceedings{cnlp:hinton86,
  author =       "G. E. Hinton",
  title =        "Learning distributed representations of concepts",
  booktitle =    "Proceedings of the 8th Annual Conference of the
                 Cognitive Science Society, Amherst, MA, August 1986",
  pages =        "1--12",
  publisher =    LEA,
  address =      LEAaddr,
  year =         "1986",
}

@InProceedings{cnlp:hinton88,
  author =       "G. E. Hinton",
  title =        "Representing part-whole hierarchies in connectionist
                 networks",
  booktitle =    "Proceedings of the 10th Annual Conference of the
                 Cognitive Science Society, Montreal, Canada, August
                 1988",
  publisher =    LEA,
  address =      LEAaddr,
  pages =        "48--54",
  year =         "1988",
}

@Article{cnlp:hinton1990,
  author =       "G. E. Hinton",
  title =        "Mapping Part Whole Hierarchies into Connectionist
                 Networks",
  journal =      "Artificial Intelligence",
  volume =       "46",
  number =       "1--2",
  pages =        "47--76",
  year =         "1990",
}

@Article{cnlp:waltz,
  author =       "D. L. Waltz and J. B. Pollack",
  title =        "Massively Parallel Parsing: {A} Strongly Interactive
                 Model of Natural Language Interpretation",
  journal =      "Cognitive Science",
  volume =       "9",
  pages =        "51--74",
  year =         "1985",
  note =         "Note: A Hybrid Connectionist/Symbolic parser",
}

@Article{cnlp:pollack,
  author =       "J. B. Pollack",
  title =        "Recursive Distributed Representations",
  journal =      "Artificial Intelligence",
  volume =       "46",
  number =       "1--2",
  pages =        "77--105",
  year =         "1990",
}

@Article{cnlp:touretzky90,
  author =       "D. S. Touretzky",
  title =        "Boltz{CONS}: Dynamic Symbol Structures in a
                 Connectionist Network",
  journal =      "Artificial Intelligence",
  volume =       "46",
  number =       "1--2",
  pages =        "5--46",
  year =         "1990",
}

@InProceedings{cnlp:pollack87a,
  author =       "J. B. Pollack",
  title =        "Cascaded back-propagation on dynamic connectionist
                 networks",
  booktitle =    "Proceedings of the 9th Annual Conference of the
                 Cognitive Science Society",
  publisher =    LEA,
  address =      LEAaddr,
  pages =        "391--404",
  year =         "1987",
}

@InCollection{cnlp:pollack89,
  author =       "J. B. Pollack",
  editor =       "D. S. Touretzky",
  title =        "Implications of recursive distributed
                 representations",
  booktitle =    "Advances in Neural Information Processing Systems",
  publisher =    "Morgan Kaufman",
  address =      "San Mateo, CA",
  pages =        "527--536",
  year =         "1989",
}

@Book{cnlp:rumelhart86a,
  author =       "D. Rumelhart and J. McClelland",
  title =        "Parallel Distributed Processing",
  volume =       "1 \& 2",
  publisher =    "MIT Press",
  address =      "Cambridge, MA",
  year =         "1986",
}

@InCollection{cnlp:rumelhart86b,
  editor =       "D. Rumelhart and J. McClelland",
  author =       "D. Rumelhart",
  title =        "On Learning the Past Tenses of English Verbs",
  booktitle =    "Parallel Distributed Processing",
  volume =       "2",
  pages =        "216--271",
  year =         "1986",
  publisher =    "MIT Press",
  address =      "Cambridge, MA",
}

@Article{cnlp:lippman,
  author =       "R. P. Lippman",
  title =        "An introduction to computing with neural networks",
  journal =      "IEEE ASSP Mag",
  volume =       "4",
  number =       "2",
  pages =        "4--22",
  year =         "1987",
}

@TechReport{cnlp:gasser,
  author =       "M. E. Gasser",
  title =        "A Connectionist Model of Sequence Generation in a
                 First and Second Language",
  institution =  "A.I. Laboratory, Computer Science Department,
                 University of California, Los Angeles",
  number =       "UCLA-AI-77-13",
  year =         "1988",
}

@InCollection{cnlp:sharkey,
  editor =       "R. Trappl",
  author =       "N. E. Sharkey",
  title =        "A {PDP} System for Goal Plan Decision",
  booktitle =    "Cybernetics and Systems",
  pages =        "1031--1038",
  publisher =    "Kluwer Academic",
  address =      "Dordrecht, The Netherlands",
  year =         "1988",
}

@InProceedings{cnlp:hanson,
  author =       "S. J. Hanson and J. Kegl",
  title =        "Parsnip:{A} connectionist network that learns natural
                 language grammar from exposure to natural language
                 sentences",
  booktitle =    "Proceedings of the Ninth Annual Conference of the
                 Cognitive Science Society, Seattle, WA, July 1987",
  publisher =    LEA,
  address =      LEAaddr,
  pages =        "106--119",
  year =         "1987",
}

@InCollection{cnlp:villegas,
  author =       "L. Villegas and R. Eberts",
  editor =       "R. Beale and J. Finlay",
  title =        "Implementing a Neural Network for a Cognitve Text
                 Editing Task.",
  booktitle =    "Neural Networks and Pattern Recognition in
                 Human-Computer Interaction",
  publisher =    "Ellis Horwood",
  address =      "Chichester, UK",
  year =         "1992",
  ISBN =         "0-13-626995-8",
}

@InCollection{cnlp:gorin91a,
  author =       "A. L. Gorin and S. E. Levinson and A. N. Gerther and
                 E. Goldman",
  title =        "Adaptive Acquisition of Language",
  booktitle =    "Neural Networks: Theory and Applications",
  publisher =    "Academic Press Inc",
  year =         "1991",
  ISBN =         "0-12-467050-4",
}

@Article{cnlp:chalmers90,
  author =       "D. J. Chalmers",
  title =        "Syntactic transformations on distributed
                 representations",
  journal =      "Connection Science",
  volume =       "2",
  number =       "1--2",
  pages =        "53--62",
  year =         "1990",
  note =         "Reprinted in \cite{cnlp:sharkey92}, pages 46--55",
}

@InProceedings{cnlp:chalmers90b,
  author =       "D. J. Chalmers",
  title =        "Why {F}odor and {P}ylyshyn Were Wrong: The Simplest
                 Refutation",
  booktitle =    "Proceedings of The Twelfth Annual Conference of the
                 Cognitive Science Society, Cambridge, MA, July 1990",
  publisher =    LEA,
  address =      LEAaddr,
  pages =        "340--347",
  year =         "1990",
}

@InCollection{cnlp:chalmers92,
  author =       "D. J. Chalmers",
  title =        "Syntactic transformations on distributed
                 representations",
  booktitle =    "Connectionist Natural Language Processing: Readings
                 from Connection Science",
  editor =       "N. E. Sharkey",
  publisher =    "Intellect",
  address =      "Oxford, UK",
  pages =        "46--55",
  year =         "1992",
}

@InProceedings{cnlp:niklasson,
  author =       "L. Niklasson and T. van Gelder",
  title =        "Can connectionist models exhibit non-classical
                 structure sensitivity?",
  booktitle =    "Proceedings of the Sixteenth Annual Conference of the
                 Cognitive Science Society, Atlanta, GA, August 1994",
  editor =       "A. Ram",
  publisher =    LEA,
  address =      LEAaddr,
  year =         "1994",
}

@InCollection{cnlp:vangelder91,
  author =       "T. van Gelder",
  title =        "What is the ``{D}'' in {PDP}? {A} survey of the
                 concept of distribution",
  booktitle =    "Philosophy and Connectionist Theory",
  editor =       "W. Ramsey and S. P. Stich and D. E. Rumelhart",
  publisher =    LEA,
  address =      LEAaddr,
  year =         "1991",
  pages =        "33--60",
}

@Article{cnlp:vangelder90,
  author =       "T. van Gelder",
  title =        "Compositionality: {A} connectionist variation on a
                 classical theme",
  journal =      "Cognitive Science",
  volume =       "14",
  year =         "1990",
  pages =        "335--364",
}

@Article{cnlp:fodor,
  author =       "J. A. Fodor and Z. W. Pylyshyn",
  title =        "Connectionism and cognitive architecture: a critical
                 analysis",
  journal =      "Cognition",
  volume =       "28",
  number =       "1--2",
  pages =        "3--71",
  bham_class_mark = "s per BF 311.A1C61",
  ISBN =         "16856465",
  year =         "1988",
}

@Article{cnlp:smolensky90,
  author =       "P. Smolensky",
  title =        "Tensor Product Variable Binding and the Representation
                 of Symbolic Structures in Connectionist Systems",
  journal =      "Artificial Intelligence",
  volume =       "46",
  number =       "1--2",
  pages =        "159--216",
  year =         "1990",
}

@Article{cnlp:elman,
  author =       "J. L. Elman",
  title =        "Finding structure in time",
  journal =      "Cognitive Science",
  volume =       "14",
  pages =        "179--211",
  year =         "1990",
}

@TechReport{cnlp:elman89,
  author =       "J. L. Elman",
  title =        "Representation and Structure in Connectionist Models",
  institution =  "University of California",
  address =      "San Diego CA",
  number =       "TR 8903",
  year =         "1989",
}

@InProceedings{cnlp:hirst,
  author =       "B. Selman and G. Hirst",
  title =        "A rule-based connectionist parsing system",
  booktitle =    "Proceedings of the Seventh Annual conference of the
                 Cognitive Science Society",
  pages =        "212--221",
  publisher =    LEA,
  address =      LEAaddr,
  year =         "1985",
}

@InProceedings{cnlp:jacquemin,
  author =       "C. Jacquemin",
  editor =       "B. Neumann",
  title =        "Activation Diffusion: {A} Connectionist Network for
                 Robust Parsing",
  booktitle =    "Proceedings of the Tenth European Conference on
                 Artificial Intelligence(ECAI92), Vienna, Austria,
                 August 1992",
  publisher =    "Wiley",
  address =      "Chichester, UK",
  pages =        "183--187",
  year =         "1992",
}

@Article{cnlp:dorffner,
  author =       "G. Dorffner",
  title =        "Review on: Neural Networks for Vision Speech and
                 Natural Language",
  journal =      "Connection Science",
  volume =       "5",
  number =       "1",
  pages =        "103--106",
  year =         "1993",
}

@InProceedings{cnlp:kimura,
  author =       "L. Kimura and T. Suzuoka and S. Amano",
  title =        "Association Based Natural Language Processing with
                 Neural Networks",
  booktitle =    "30th Annual Meeting of the Association for
                 Computational Linguistics, Newark, DE, June 1992",
  publisher =    "Association for Computational Linguistics",
  pages =        "224--231",
  year =         "1992",
}

@Book{cnlp:Linggard,
  editor =       "R. Linggard and D. J. Myers and C. Nightingale",
  title =        "Neural Networks for Vision, Speech and Natural
                 Language",
  publisher =    "Chapman and Hall",
  address =      "London",
  year =         "1992",
}

@InCollection{cnlp:lyon92,
  author =       "C. Lyon and R. Frank",
  title =        "Detecting Structures in Natural Language Using Neural
                 Net and Rules",
  booktitle =    "Artificial Neural Networks 2",
  editor =       "I. Aleksander and J. Taylor",
  publisher =    "North-Holland",
  address =      "Amsterdam",
  pages =        "731--734",
  year =         "1992",
}

@InProceedings{cnlp:scholtes91c,
  author =       "J. C. Scholtes",
  title =        "Using Extended {K}ohonen-Feature Maps in a Language
                 Acquisition Model",
  booktitle =    "Proceedings of the 2nd Australian Conference on Neural
                 Nets, Sydney, Australia, February 2-4",
  year =         "1991",
  pages =        "38--43",
}

@InProceedings{cnlp:scholtes91d,
  author =       "J. C. Scholtes",
  title =        "Learning Simple Semantics by Self-Organization",
  booktitle =    "Worknotes of the AAAI Spring Symp. Series on Machine
                 Learning of Natural Language and Ontology, Palo Alto,
                 CA, March 26-29",
  year =         "1991",
  pages =        "146--151",
  annote =       "page numbers may be incorrect",
}

@InProceedings{cnlp:scholtes91e,
  author =       "J. C. Scholtes",
  title =        "Unsupervised Context Learning in Natural Language
                 Processing",
  booktitle =    "Proceedings of the IJCNN, Seattle, WA, July 8-12",
  year =         "1991",
  volume =       "1",
  pages =        "107--112",
  abstract =     "By generalizing over contextual information, excellent
                 results were obtained in connectionist language
                 processing. Normally, these contexts are added manually
                 to the system or deducted by using a supervised
                 learning algorithm. A recurrent self-organizing model,
                 capable of deriving the context from scratch, is
                 presented. It is shown that syntactic features and
                 structures are learned in a unsupervised way from flat
                 sentences. By generalizing over the words as well as
                 the sentences, simple semantics can be derived. The
                 model forms a two-layer extension of the Kohonen
                 feature map, provided with additional recurrent fibers
                 which are responsible for the automatic determination
                 of word contexts, thus resulting in a unsupervised
                 recurrent learning algorithm. After a formal
                 description of the model, the experimental results are
                 presented. 19 Refs.",
  keywords =     "Learning Systems - Performance Computer Programming -
                 Algorithms Automata Theory Natural Language Processing
                 Learning Algorithms Semantics Syntax Kohonen Feature
                 MAPS WORD Contexts",
}

@InProceedings{cnlp:scholtes91f,
  author =       "J. C. Scholtes",
  title =        "Kohonen's Self-Organizing Map in Natural Language
                 Processing",
  booktitle =    "Proceedings of the SNN Symposium, Nijmegen, The
                 Netherlands, May 1-2",
  year =         "1991",
  pages =        "64",
}

@InProceedings{cnlp:scholtes91g,
  author =       "J. C. Scholtes",
  title =        "Kohonen's Self-Organizing Map Applied Towards Natural
                 Language Processing",
  booktitle =    "Proceedings of the CUNY 1991 Conference on Sentence
                 Processing, Rochester, NY, May 12-14",
  year =         "1991",
  pages =        "10",
}

@TechReport{cnlp:scholtes91h,
  author =       "Scholtes J. C.",
  title =        "Kohonen Feature Maps in Natural Language Processing",
  institution =  "Dept of Computation Linguistics, University of
                 Amsterdam",
  year =         "1991",
}

@Book{cnlp:reilly,
  editor =       "R. G. Reilly and N. E. Sharkey",
  title =        "Connectionist approaches to Natural Language
                 Processing",
  publisher =    LEA,
  address =      LEAaddr,
  year =         "1992",
  ISBN =         "0863771793",
}

@Book{cnlp:hinton1990a,
  editor =       "G. E. Hinton",
  title =        "Connectionist Symbol Processing",
  publisher =    "MIT Press/Elsevier",
  address =      "Cambridge, MA",
  year =         "1991",
}

@Article{cnlp:sharkey91,
  author =       "N. E. Sharkey",
  title =        "Connectionist Representation Techniques",
  journal =      "Artificial Intelligence Review",
  volume =       "5",
  number =       "3",
  year =         "1991",
}

@Book{cnlp:sharkey92,
  editor =       "N. E. Sharkey",
  title =        "Connectionist Natural Language Processing:Readings
                 from Connection Science",
  publisher =    "Intellect",
  address =      "Oxford, UK",
  year =         "1992",
}

@PhdThesis{cnlp:plate,
  author =       "T. A. Plate",
  title =        "Distributed Representations and Nested Compositional
                 Structure",
  school =       "University of Toronto",
  year =         "1994",
}

@InProceedings{cnlp:plate91,
  author =       "T. A. Plate",
  editor =       "J. Mylopoulos and R. Reiter",
  title =        "Holographic {R}educed {R}epresentations: {C}onvolution
                 Algebra for Compositional Distributed Representations",
  pages =        "30--35",
  booktitle =    "Proceedings of the 12th International Joint Conference
                 on Artificial Intelligence, Sydney, Australia, August
                 1991",
  publisher =    "Morgan Kauffman",
  address =      "San Mateo, CA",
  year =         "1991",
  note =         "Reprinted in Mehra P. and Wah B.W. (editors).
                 Artificial Neural Networks: Concepts and Theory, Los
                 Alamitos, CA, IEEE Computer Society Press 1992",
}

@Article{cnlp:plate95,
  author =       "T. A. Plate",
  title =        "Holographic Reduced Representations",
  journal =      "IEEE Transactions on Neural Networks",
  volume =       "6",
  number =       "3",
  pages =        "623--641",
  year =         "1995",
}

@InProceedings{cnlp:plate93a,
  author =       "T. A. Plate",
  title =        "Holographic Recurrent Networks",
  booktitle =    "Advances in Neural Information Processing Systems 5:
                 NIPS * 92, Denver, CO, November 1992",
  editor =       "C. L. Giles and S. J. Hanson and J. D. Cowan",
  publisher =    "Morgan Kaufmann",
  address =      "San Mateo, CA",
  pages =        "34--41",
  year =         "1993",
}

@InProceedings{cnlp:plate94,
  author =       "T. A. Plate",
  title =        "Estimating structural similarity by vector
                 dot-products of Holographic Reduced Representations",
  booktitle =    "Advances in Neural Information Processing Systems 6
                 NIPS * 93, Denver, CO, November 1993",
  editor =       "J. D. Cowan and G. Tesauro and J. Alspector",
  publisher =    "Morgan Kaufmann",
  pages =        "1109--1116",
  year =         "1994",
}

@Article{cnlp:collier,
  author =       "R. Collier",
  title =        "An Historical Overview of Natural Language Processing
                 Systems that Learn",
  journal =      "Artificial Intelligence Review",
  volume =       "8",
  number =       "1",
  year =         "1994",
}

@TechReport{cnlp:miikkulainen93,
  author =       "R. Miikkulainen",
  title =        "Subsymbolic Case-Role Analysis of Sentences with
                 Embedded Clauses",
  institution =  "University of Texas at Austin",
  address =      "Austin, TX 78712",
  number =       "AI 93-202",
  year =         "1993",
}

@PhdThesis{cnlp:henderson,
  author =       "J. B. Henderson",
  title =        "Description Based Parsing in a Connectionist Network",
  school =       "University of Pennysylvania",
  year =         "1994",
}

@TechReport{cnlp:fanty85,
  author =       "M. Fanty",
  title =        "Context-Free Parsing in Connectionist Networks",
  institution =  "Dept of Computer Science, University of Rochester,
                 NY",
  number =       "TR-174",
  year =         "1985",
}

@InProceedings{cnlp:marshall,
  author =       "J. A. Marshall",
  title =        "Development of Perceptual Context-Sensitivity in
                 Unsupervised Neural Networks: Parsing, Grouping and
                 Segmentation",
  booktitle =    "International Joint Conference on Neural Networks,
                 Baltimore, MD, 1992",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  pages =        "315--320",
  year =         "1992",
}

@InProceedings{cnlp:nijholt,
  author =       "A. Nijholt",
  editor =       "R. Trappl",
  title =        "Meta-Parsing in Neural Networks",
  booktitle =    "Cybernetics and Systems '90",
  publisher =    "World Scientific Publishing",
  address =      "Singapore",
  pages =        "969--976",
  year =         "1990",
}

@InProceedings{cnlp:scholtes92,
  author =       "J. C. Scholtes and S. Bloembergen",
  title =        "The Design of a Neural Data-Oriented Parsing ({DOP})
                 System",
  booktitle =    "IJCNN, International Joint Conference on Neural
                 Networks",
  publisher =    "Baltimore, IEEE",
  pages =        "69--74",
  year =         "1992",
}

@InProceedings{cnlp:lakoff,
  author =       "G. Lakoff",
  title =        "A Suggestion for {A} Linguistics with Connectionist
                 Foundations",
  editor =       "D. S. Touretzky",
  booktitle =    "Proceedings of the 1988 Connectionist Models Summer
                 School",
  publisher =    "Morgan Kaufmann",
  address =      "Los Altos,CA",
  pages =        "301--310",
  year =         "1988",
}

@TechReport{cnlp:diederich,
  author =       "J. Diederich",
  title =        "Spreading Activation and Connectionist Models for
                 Natural Language Processing",
  institution =  "International Computer Science Institute(ICSI)",
  address =      "Berkeley, California",
  number =       "TR-89-008",
  year =         "1989",
}

@InProceedings{cnlp:dorffner91,
  author =       "G. Dorffner",
  title =        "'Radical' Connectionism for Natural Language
                 Processing",
  booktitle =    "Working Notes of the AAAI Symposium on Connectionist
                 Natural Language Processing",
  year =         "1991",
}

@Article{cnlp:jacquemin94,
  author =       "C. Jacquemin",
  title =        "A Temporal Connectionist Approach to Natural
                 Language",
  journal =      "Sigart Bulletin",
  publisher =    "ACM Press",
  volume =       "5",
  number =       "3",
  year =         "1994",
}

@InProceedings{cnlp:kamimura,
  author =       "R. Kamimura",
  title =        "Application of Temporal Supervised Learning Algorithm
                 to Generation of Natural Language",
  booktitle =    "International Joint Conference on Neural Networks,
                 Washington DC, January 1990",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  volume =       "1",
  pages =        "201--208",
  year =         "1990",
  abstract =     "An attempt is made to generate natural language by
                 using a recurrent neural network with the temporal
                 supervised learning algorithm (TSLA), developed by R.
                 J. Williams and D. Zipser (1989). As TSLA uses explicit
                 representation of consecutive events, it can deal with
                 time-changing phenomena without increasing the number
                 of units in the network. However, its performance has
                 been evaluated exclusively upon the limited short
                 sequences or sequences with explicit regularity and not
                 for the sequences of natural language, which show
                 complex and long-distance correlation. It was found
                 that TSLA showed extreme instability in the learning
                 process, and it took a long time to finish the
                 learning. Thus, the author proposes two methods to
                 improve the performance of TSLA. The first is the
                 variable learning rate method, which is used to remove
                 the instability of the learning process. The second is
                 Minkowski-r power metrics, which is used to improve the
                 learning time. It was found from experiments that the
                 variable learning methods could remove the instability
                 completely and that Minkowski-r power metrics could
                 significantly improve the learning time. The number of
                 hidden units, necessary for generating a long sequence
                 of natural language, seems to reach gradually a stable
                 point, where the number of hidden units is relatively
                 small compared with the sequence length of natural
                 language. This shows that TSLA can compress the
                 information of long and complex sequences into a
                 relatively small number of static hidden units. 9
                 Refs.",
  keywords =     "Natural Language Processing Systems - Performance
                 Neural Networks Learning Systems Learning Algorithms",
}

@InCollection{cnlp:sharkey90,
  author =       "N. Sharkey",
  title =        "Connectionist Representation for Natural Language: Old
                 and New",
  editor =       "G Dorffner",
  booktitle =    "Konnektionismus in Artificial Intelligence und
                 Kognitionsforschung",
  publisher =    "Springer",
  address =      "Berlin",
  year =         "1990",
}

@Book{cnlp:ward,
  author =       "N. Ward",
  title =        "A Connectionist Natural Language Generator",
  publisher =    "Ablex",
  address =      "Norwood, N.J.",
  year =         "1994",
  note =         "Revised and extended version of {\em A Flexible,
                 Parallel Model of Natural Language Generation}, Ph.D.
                 thesis and Technical Report UCB--CSD 91/629, Computer
                 Science Division, University of California at
                 Berkeley",
}

@Unpublished{cnlp:hammerton94,
  author =       "J. A. Hammerton",
  title =        "A Hybrid Connectionist Shift Reduce Parser",
  note =         "Note: Final Year Project Report, Departments of
                 Artificial Intelligence and Computer Science, The
                 University of Edinburgh",
  month =        jun,
  year =         "1994",
}

@Book{cnlp:minsky,
  author =       "M. L. Minsky and S. Papert",
  title =        "Perceptrons",
  address =      "Cambridge MA",
  publisher =    "MIT Press",
  year =         "1969",
}

@Article{cnlp:nenov93,
  title =        "Perceptually Grounded Language Learning: Part 1-{A}
                 Neural Network Architecture for Robust Sequence
                 Association",
  author =       "V. I. Nenov and M. G. Dyer",
  journal =      "Connection Science",
  year =         "1993",
  volume =       "5",
  number =       "2",
}

@Article{cnlp:nenov,
  title =        "Perceptually Grounded Language Learning: Part
                 2-{DETE}: {A} Neural/Procedural Model",
  author =       "V. I. Nenov and M. G. Dyer",
  journal =      "Connection Science",
  year =         "1994",
  volume =       "6",
  number =       "1",
  ISBN =         "0954-0091",
}

@Article{cnlp:gupta,
  title =        "Connectionist Models and Linguistic Theory:
                 Investigations of Stress Systems in Language",
  author =       "P. Gupta and D. S. Touretzky",
  journal =      "Cognitive Science",
  year =         "1994",
  volume =       "18",
  number =       "1",
  ISBN =         "0364-0213",
}

@Article{cnlp:sharkey94a,
  title =        "Connectionist advances in natural language
                 processing",
  author =       "N. Sharkey",
  journal =      "Colloquium Digest -- IEE",
  year =         "1994",
  number =       "38",
}

@Article{cnlp:lyon93,
  title =        "Using neural networks to infer grammatical structures
                 in natural language",
  author =       "C. Lyon",
  journal =      "Colloquium Digest -- IEE",
  year =         "1993",
  number =       "92",
}

@Article{cnlp:shastri,
  title =        "From simple associations to systematic reasoning: a
                 connectionist representation of rules, variables and
                 dynamic bindings using temporal synchrony",
  author =       "L. Shastri and V. Ajjanagadde",
  journal =      "Behavioural and Brain Sciences",
  year =         "1993",
  volume =       "16",
  pages =        "417--494",
  number =       "3",
}

@Article{cnlp:barnden91,
  title =        "Encoding Techniques for Complex Information Structures
                 in Connectionist Systems",
  author =       "J. A. Barnden and Kankanahalli Srinivas",
  journal =      "Connection Science",
  volume =       "3",
  number =       "3",
  year =         "1991",
}

@Article{cnlp:barnden93,
  title =        "Connectionist meta-representation for propositional
                 attitudes",
  author =       "J. A. Barnden",
  journal =      "Journal of Experimental and Theoretical Artificial
                 Intelligence",
  year =         "1993",
  volume =       "5",
  number =       "2--3",
}

@Article{cnlp:surkan,
  title =        "Coding of Natural Language Task Descriptions prior to
                 Their Classification by Neural Networks",
  author =       "A. J. Surkan and R. M. Evans",
  journal =      "Proceedings Of The Hawaii International Conference On
                 System Sciences",
  year =         "1993",
  volume =       "26//V4",
}

@Article{cnlp:utsumi,
  title =        "Natural Language Interfase using Contextual
                 Information by Connectionist Model",
  author =       "A. Utsumi and K. Hori and S. Ohsuga",
  journal =      "Japanese Society For Artificial Intelligence",
  year =         "1992",
  volume =       "7",
  number =       "5",
}

@Article{cnlp:moisl,
  title =        "Connectionist Finite State Natural Language
                 Processing",
  author =       "H. Moisl",
  journal =      "Connection Science",
  year =         "1992",
  volume =       "4",
  number =       "2",
}

@InProceedings{cnlp:wu,
  title =        "Word sense disambiguation by a higher order
                 connectionist net based on distributed
                 representations",
  author =       "X. Wu and M. McTear and P. Ojha",
  booktitle =    "Proceedings of the 1993 IEEE Region 10 Conference on
                 Computer, Communication, Control aand Power Engineering
                 (Tencon '93). Part 2 (of 5) Beijing, China Oct 19-21
                 1993 CC20220 IEEE",
  year =         "1993",
  pages =        "893--897",
  ISBN =         "0-7803-1233-3",
  address =      "Univ of Ulster at Jordanstown, Ireland",
  abstract =     "Word sense disambiguation is one of the most
                 challenging areas in natural language processing. In
                 this paper, we describe a higher order neural net based
                 on a distributed representation method for word sense
                 disambiguation. Two types of theory of neural
                 representations, which are localist and distributed
                 representations, are discussed and compared. The
                 concept of microfeatures which typically belongs to the
                 category of distributed representation is introduced
                 and incorporated in the system. We will show that a
                 system based on a distributed representation is
                 potentially more effective than that based on a
                 localist representation in solving the word sense
                 disambiguation problem. (Author abstract) 18 Refs.",
  keywords =     "Neural networks Word sense disambiguation Higher order
                 connectionist net Neural representation theories _723
                 _921",
}

@Article{cnlp:chandola,
  title =        "Ordered rules for full sentence translation: a neural
                 network realization and a case study for {H}indi and
                 {E}nglish",
  author =       "A. Chandola and A. Mahalanobis",
  journal =      "Pattern Recognition",
  year =         "1994",
  volume =       "27",
  number =       "4",
  pages =        "515--521",
  publisher =    "Pergamon Press Inc, Tarrytown, New York, USA",
  ISBN =         "0031-3203",
  address =      "Univ of Arizona, Tucson, Arizona, USA",
  abstract =     "As a general tool for pattern recognition, neural
                 networks have made an impact in various fields which
                 require automation and learning. Their use is
                 demonstrated for learning ordered rules which make it
                 possible to translate one language into another. Using
                 pattern recognition techniques, it is shown that neural
                 networks can implement such rules by learning to
                 reorder the words in a sentence appropriately. First it
                 is described how the ordered rules yield a situational
                 translation from one language to another. Illustrative
                 examples are given in English and Hindi, two
                 Indo-European languages. Then the neural network
                 realization of the translation algorithm is discussed.
                 The work presented here may serve as a model for other
                 languages. (author abstract) 7 Refs.",
  keywords =     "Pattern recognition Linguistics Translation
                 (languages) Neural networks Learning systems Algorithms
                 Computer applications Ordered rules Full sentence
                 translation Phrase order Situation Reordering Coded
                 input/output Hindi",
}

@Article{cnlp:kita,
  title =        "Spoken sentence recognition based on {HMM}-{LR} with
                 hybrid language modeling",
  author =       "K. Kita and T. Morimoto and K. Ohkura and S. Sagayama
                 and Y. Yano",
  journal =      "Transactions on Information and Systems 94 VE",
  year =         "??",
  number =       "2",
  pages =        "258--265",
  publisher =    "Inst of Electronics, Inf \& Commun Engineers of Japan,
                 Tokyo, Japan",
  ISBN =         "0916-8532",
  address =      "Tokushima Univ, Tokushima-shi, Japan",
  abstract =     "This paper describes Japanese spoken sentence
                 recognition using hybrid language modeling, which
                 combines the advantages of both syntactic and
                 stochastic language models. As the baseline system, we
                 adopted the HMM-LR speech recognition system, with
                 which we have already achieved good performance for
                 Japanese phrase recognition tasks. Several improvements
                 have been made to this system aimed at handling
                 continuously spoken sentences. The first improvement is
                 HMM training with continuous utterances as well as word
                 utterances. In previous implementations, HMMs were
                 trained with only word utterances. Continuous
                 utterances are included in the HMM training data
                 because coarticulation effects are much stronger in
                 continuous utterances. The second improvement is the
                 development of a sentential grammar for Japanese. The
                 sentential grammar was created by combining inter- and
                 intra-phrase CFG grammars, which were developed
                 separately. The third improvement is the incorporation
                 of stochastic linguistic knowledge, which includes
                 stochastic CFG and a bigram model of production rules.
                 The system was evaluated using continuously spoken
                 sentences from a conference registration task that
                 included approximately 750 words. We attained a
                 sentence accuracy of 83.9% in the speaker-dependent
                 condition. (author abstract) 23 Refs.",
  keywords =     "Speech recognition Natural language processing systems
                 Computer simulation Computational linguistics Neural
                 networks Context free grammars Acoustics Speech
                 analysis Hybrid language modeling HMM-LR speech
                 recognition Stochastic linguistic knowledge LR sparsing
                 Hidden Markov model Syntactic language model Acoustic
                 information Stochastic language model",
}

@InProceedings{cnlp:law,
  title =        "Neural network-assisted Japanese-English machine
                 translation system",
  author =       "T. Law and H. Itoh and H. Seki",
  booktitle =    "Proceedings of 1993 International Joint Conference on
                 Neural Networks. Part 3 (of 3) Nagoya, Jpn Oct 25,
                 1993",
  year =         "1993",
  volume =       "3",
  pages =        "2905--2908",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  ISBN =         "0-7803-1421-2",
  abstract =     "In this paper, we present a hybrid machine translation
                 system which combines the strengths of logic
                 programming, procedural programming, and neural
                 networks. The system is designed for Japanese to
                 English translation of natural language sentences found
                 in daily newspaper weather reports. The system is
                 trained on one full year of weather reports, or over
                 1000 sentences. A second full year of weather reports
                 is used to empirically evaluate the system. (author
                 abstract) 5 Refs.",
  keywords =     "Computer aided language translation Linguistics Neural
                 networks Logic programming Natural language processing
                 systems Artificial intelligence Expert systems Systems
                 analysis Hybrid machine translation system",
}

@InProceedings{cnlp:wang93,
  title =        "Simple recurrent network for Chinese word prediction",
  author =       "M. Wang and W. Liu and Y. Zhong",
  year =         "1993",
  volume =       "1",
  pages =        "263--266",
  publisher =    "IEEE, Piscataway, New Jersey, USA (IEEE cat n
                 93CH3353-0)",
  ISBN =         "0-7803-1421-2",
  address =      "Beijing Univ, Beijing, China",
  booktitle =    "Proceedings of 1993 International Joint Conference on
                 Neural Networks. Part 1 (of 3) Nagoya, Jpn Oct 25-25",
  abstract =     "This paper presents preliminary investigations
                 concerning the use of Simple Recurrent Network (SRN) in
                 Chinese word prediction. We explore the architecture
                 introduced by J.L.Elman for predicting successive
                 elements of a sequence. This model is based on a
                 multi-layer architecture and contains special units,
                 called context units which provide the short-term
                 memory(STM) in the system. Based on this model, We
                 constructed a modular SRNs to predict Chinese word at
                 two levels. The first level network predicts the major
                 category of the next word, then the next possible word
                 is predicted at the second level network. Also, the
                 specific encoding schemes was described in the paper.
                 Experiments show that the method is promising. (author
                 abstract) 5 Refs.",
  keywords =     "Neural networks Word processing Computer architecture
                 Encoding (symbols) Computer simulation Data storage
                 equipment Natural language processing systems Simple
                 recurrent network Chinese word prediction Short term
                 memory Word prediction problem High level cognitive
                 processing Time sequence",
}

@InProceedings{cnlp:kawahara,
  title =        "Neural network approach to inference mechanism for
                 logic programming language",
  author =       "H. Kawahara and H. Murakoshi and N. Funakubo and S.
                 Ishijima",
  year =         "1993",
  volume =       "1",
  pages =        "167--170",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  ISBN =         "0-7803-1421-2",
  booktitle =    "Proceedings of 1993 International Joint Conference on
                 Neural Networks. Part 1 (of 3) Nagoya, Japan Oct
                 25--29",
  abstract =     "We present a new inference mechanism for logic
                 programming languages using neural networks that is
                 flexible and suited for fine-grain parallel computing.
                 Our approach is radically different from the
                 conventional methods based on refutation processes.
                 Programs written in logic programming language is
                 transformed into a Hopfield-type neural network. And
                 relaxation techniques are applied to this network to
                 inference solutions. We proposed an algorithm to
                 transform logic programs into Hopfield-type neural
                 networks and implemented a prototype of the inference
                 system based on this mechanism. And we tested the
                 system with some preliminary problems. This preliminary
                 results confirm us that our algorithm is correct.
                 (author abstract) 4 Refs.",
  keywords =     "Neural networks Inference engines Logic programming
                 Parallel processing systems Algorithms Relaxation
                 processes Optimization Computer programming languages
                 Computational methods Logic programming language
                 Parallel computing Refutation processes Energy function
                 Hopfield type neural network",
}

@InProceedings{cnlp:tsunoda,
  title =        "Semantic ambiguity resolution by parallel distributed
                 associative inference and contradiction detection",
  author =       "T. Tsunoda and H. Tanaka",
  year =         "1993",
  volume =       "1",
  pages =        "163--166",
  publisher =    "IEEE, Piscataway, New Jersey, USA (IEEE cat n
                 93CH3353-0)",
  ISBN =         "0-7803-1421-2",
  address =      "Univ of Tokyo, Tokyo, Japan",
  booktitle =    "Proceedings of 1993 International Joint Conference on
                 Neural Networks. Part 1 (of 3) Nagoya, Jpn Oct 25-29",
  abstract =     "In this paper we propose a PDAI&CD architecture aimed
                 at constructing natural inference systems. The kernel
                 consists of a mutually associative neural network which
                 processes numerical patterns and of a logical system
                 processing symbols. The associative part calls on
                 context-dependent free-association of concepts based on
                 the relations of concepts acquired from a dynamically
                 changing outer world. In the logical part of the
                 architecture, the results obtained by the neural
                 network are checked, and emerging contradictions create
                 feedback to the associative network and thus find a
                 final optimum solution. WAVE, a concrete implemented
                 system, is introduced here, and we show its application
                 to the problem of ambiguity resolution in natural
                 language understanding. (author abstract) 3 Refs.",
  keywords =     "Inference engines Neural networks Database systems
                 Computational linguistics Data acquisition Computer
                 architecture Codes (symbols) Parallel processing
                 systems Natural language processing systems Context
                 sensitive languages Semantic ambiguity resolution
                 Natural inference systems Kernel Logical system
                 processing Syntactics Winner associative voting
                 engine",
}

@Article{cnlp:sun93b,
  title =        "Efficient feature-based connectionist inheritance
                 scheme",
  author =       "R. Sun",
  journal =      "IEEE Transactions on Systems, Man and Cybernetics",
  year =         "1993",
  volume =       "23",
  number =       "2",
  pages =        "512--522",
  ISBN =         "0018-9472",
  address =      "Univ of Alabama, Tuscaloosa, Alabama, USA",
  abstract =     "A connectionist model that deals with the inheritance
                 problem in an efficient and natural way is described.
                 Based on the connectionist architecture Consyderr, the
                 problem of property inheritance and formulate it in
                 ways facilitating conceptual clarity and connectionist
                 implementation is analyzed. A set of `benchmarks' is
                 specified for ensuring the correctness of solution
                 mechanisms; parameters of Consyderr are formally
                 derived to satisfy these benchmark requirements. The
                 paper also discusses how chaining of is-a links and
                 multiple inheritance can be handled in this
                 architecture. It is shown that Consyderr with a
                 two-level dual (localist and distributed)
                 representation can handle inheritance and cancellation
                 of inheritance correctly and extremely efficiently, in
                 constant time instead of proportional to the length of
                 a chain in an inheritance hierarchy. It also
                 demonstrates the utility of a meaning-oriented
                 intentional approach (with features), for supplementing
                 and enhancing extensional approaches. (author abstract)
                 31 Refs.",
  keywords =     "Artificial intelligence Data structures Pattern
                 recognition Algorithms Knowledge based systems
                 Hierarchical systems Systems analysis Inference engines
                 Fuzzy sets Mathematical models Computer architecture
                 Efficient feature based connectionist scheme
                 Connectionist architecture Consyderr",
}

@InProceedings{cnlp:kock,
  title =        "Connectionist model description: {A} case study",
  author =       "G. Kock and N. B. Serbedzija",
  year =         "1993",
  pages =        "843--850",
  ISBN =         "0165-6074",
  booktitle =    "19th Euromicro Symposium on Microprocessing and
                 Microprogramming, Barcelona, Spain, September 6-9,
                 1993",
  abstract =     "An approach to provide high-level specification of
                 neural networks is presented in this paper and
                 illustrated on the well known `truck backer-upper'
                 problem. The developed specification language MAX is
                 based upon the modified AXON model and provides
                 readable descriptions of common connectionist models.
                 The descriptions contain all relevant information
                 concerning connectionist programming but are free from
                 implementation details. Further refinement and
                 implementation strategy is also outlined. (author
                 abstract) 8 Refs.",
  keywords =     "Neural networks Computer programming Computer hardware
                 description languages Mathematical models Control
                 systems Computer architecture Computer simulation C
                 (programming language) Programming theory Connectionist
                 model description Truck backer upper problem
                 Specification language MAX",
}

@InProceedings{cnlp:baldwin,
  title =        "Connectionist vs. symbolic feature representation in
                 evidential support logic",
  author =       "J. F. Baldwin and M. R. Coyne and T. P. Martin",
  year =         "1993",
  pages =        "827--832",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  ISBN =         "0-7803-0615-5",
  booktitle =    "Second IEEE International Conference on Fuzzy Systems
                 San Francisco, CA, March 28--April 1, 1993",
  abstract =     "The use of evidential support logic in deductive
                 reasoning is introduced. Two methods of matching
                 features within objects are considered which are based
                 on a symbolic and a sub-symbolic approach. The two are
                 compared in a small example which uses four
                 hand-written characters, training is performed over a
                 small set of 'perfect' examples and a set of untrained
                 test cases are considered. The conclusions suggest that
                 although both methods have certain merits, the
                 combination of the advantages of each are possible due
                 to the transparency of the feature mapping method to
                 the rest of the evidential support engine. (author
                 abstract) 12 Refs",
  keywords =     "Automata theory Logic design Fuzzy sets Character
                 recognition Image processing Evidential support logic
                 Feature representation Deductive reasoning Feature
                 mapping",
}

@Article{cnlp:romaniuk,
  title =        "{SC}-net: {A} hybrid connectionist, symbolic system",
  author =       "S. G. Romaniuk and L. O. Hall",
  journal =      "Information Sciences",
  year =         "1993",
  volume =       "71",
  number =       "3",
  pages =        "223--268",
  ISBN =         "0020-0255",
  address =      "Univ of South Florida, Tampa, Florida, USA",
  abstract =     "This paper describes the SC-net system that has been
                 developed to provide expert systems capability
                 augmented with learning in a hybrid
                 connectionist/symbolic approach. A distributed
                 connectionist representation of cells connected by
                 links is used to represent symbolic knowledge. Rules
                 may be directly encoded in the connectionist network or
                 learned from examples. The learning method is a form of
                 instance-based learning in which some of the individual
                 instances in the training set are encoded by adding
                 structure to the network and others cause modifications
                 to biases in the network. Both continuous and nominal
                 attributes are directly represented in the network
                 structure. A limited form of variables in the form of
                 attribute value bindings on the right-hand side of
                 rules is supported. Relational comparators in the form
                 of cell groups are also supported. Relational
                 comparators and attribute value structures are
                 represented by groups of connected cells in the
                 network. The learning algorithm is presented and
                 methods for providing generalization in an
                 instance-based connectionist environment are presented.
                 Empirical results are presented, which include learning
                 in domains (fevers and gems) that contain uncertainty
                 and the well-known iris, and soybean data sets together
                 with a real world domain for semiconductor wafer fault
                 diagnosis. The generalization ability of the learned
                 network is shown to be good in several domains
                 including iris. The system is shown to compare
                 favorably with a nonneural instance-based learning
                 algorithm IBL. (author abstract) 49 Refs",
  keywords =     "Learning systems Neural networks Expert systems
                 Algorithms Encoding (symbols) Data structures
                 Relational comparators Learning algorithms",
}

@Article{cnlp:fu,
  title =        "Knowledge-based connectionism for revising domain
                 theories",
  author =       "L. M. Fu",
  journal =      "IEEE Transactions on Systems, Man and Cybernetics",
  year =         "1993",
  volume =       "23",
  number =       "1",
  pages =        "173--182",
  ISBN =         "0018-9472",
  address =      "Univ of Florida, Gainesville, Florida, USA",
  abstract =     "Integration of domain theory into empirical learning
                 is important in building a useful learning system in
                 practical domains since the theory is not always
                 perfect and the data is not always adequate. A novel
                 knowledge-based connectionist model referred to as
                 Kbcnn for machine learning is presented. In the Kbcnn
                 learning model, useful domain attributes and concepts
                 are first identified and linked in a way consistent
                 with initial domain knowledge, and then the links are
                 weighted properly so as to maintain the semantics.
                 Hidden units and additional connections may be
                 introduced into this initial connectionist structure as
                 appropriate. Then, this primitive structure evolves to
                 minimize empirical error. The Kbcnn learning model
                 allows the theory learned or revised to be translated
                 into the symbolic rule-based language that describes
                 the initial theory. Thus, a domain theory can be pushed
                 onto the network, revised empirically over time, and
                 decoded in symbolic form. The domain of molecular
                 genetics has been used to demonstrate the validity of
                 the Kbcnn learning model and its superiority over
                 related learning methods. (author abstract) 30 Refs",
  keywords =     "Knowledge based systems Learning systems Neural
                 networks Cognitive systems Domain theory Empirical
                 learning Machine learning Knowledge based connectionist
                 models Rule based systems",
}

@Article{cnlp:maclennan,
  title =        "Characteristics of connectionist knowledge
                 representation",
  author =       "B. Maclennan",
  journal =      "Information Sciences",
  year =         "1993",
  volume =       "70",
  number =       "1--2",
  pages =        "119--143",
  ISBN =         "0020-0255",
  address =      "Univ of Tennessee, Knoxville, TE, USA",
  abstract =     "Connectionism -- the use of neural networks for
                 knowledge representation and inference -- has profound
                 implications for the representation and processing of
                 information because it provides a fundamentally new
                 view of knowledge. However, its progress is impeded by
                 the lack of a unifying theoretical construct
                 corresponding to the idea of a calculus (or formal
                 system) in traditional approaches to knowledge
                 representation. Such a construct, called a simulacrum,
                 is proposed here, and its basic properties are
                 explored. We find that although exact classification is
                 impossible, several other useful, robust kinds of
                 classification are permitted. The representation of
                 structured information and constituent structure are
                 considered, and we find a basis for more flexible
                 rule-like processing than that permitted by
                 conventional methods. We discuss briefly logical issues
                 such as decidability and computability and show that
                 they require reformulation in this new context.
                 Throughout, we discuss the implications of this new
                 theoretical framework for artificial intelligence and
                 cognitive science. (author abstract) 31 Refs",
  keywords =     "Neural networks Knowledge based systems Artificial
                 intelligence Formal logic Computability and
                 decidability Learning systems Adaptive systems
                 Cognitive systems Pattern recognition Combinatorial
                 mathematics Connectionist networks Connectionist
                 knowledge representation Flexible information
                 processing Connectionism constructs Cognitive science",
}

@InProceedings{cnlp:berg,
  title =        "Connectionist parser with recursive sentence structure
                 and lexical disambiguation",
  author =       "G. Berg",
  publisher =    "AAAI",
  year =         "1992",
  pages =        "32--37",
  address =      "Menlo Park, CA",
  ISBN =         "0-262-51063-4",
  booktitle =    "Proceedings of the Tenth National Conference on
                 Artificial Intelligence - AAAI-92, San Jose, CA, July
                 1992",
  abstract =     "In order to be taken seriously, connectionist natural
                 language processing systems must be able to parse
                 syntactically complex sentences. Current connectionist
                 parsers either ignore structure or impose prior
                 restrictions on the structural complexity of the
                 sentences they can process - either number of phrases
                 or the 'depth' of the sentence structure. Xeric
                 networks, presented here, are distributed
                 representation connectionist parsers which can analyze
                 and represent syntactically varied sentences, including
                 ones with recursive phrase structure constructs. No a
                 priori limits are placed on the depth or length of
                 sentences by the architecture. Xeric networks use
                 recurrent networks to read words one at a time.
                 RAAM-style reduced description and X-Bar grammar are
                 used to make an economical syntactic representation
                 scheme. This is combined with a training technique
                 which allows Xeric to use multiple, virtual copies of
                 its RAAM decoder network to learn to parse and
                 represent sentence structure using gradient-descent
                 methods. Xeric networks also perform number-person
                 disambiguation and lexical disambiguation. Results show
                 that the networks works train to a few percent error
                 for sentences up a phrase-nesting depth of ten or more
                 and that this performance generalizes well. (author
                 abstract) 11 Refs",
  keywords =     "Artificial intelligence Computer programming languages
                 Computer systems programming Word processing
                 Connectionist parser Recursive sentence structure
                 Lexical disambiguation Xeric networks Natural language
                 processing Phrase nesting depth RAAM decoder network.",
}

@InProceedings{cnlp:gorin91b,
  title =        "On adaptive acquisition of spoken language",
  author =       "A. L. Gorin and S. E. Levinson and L. G. Miller and A.
                 N. Gertner",
  year =         "1991",
  pages =        "422--431",
  publisher =    "IEEE",
  address =      "New York, USA",
  ISBN =         "0-7803-0118-8",
  booktitle =    "Proceedings of the 1991 Workshop on Neural Networks
                 for Signal Processing - NNSP-91, Princeton, NJ,
                 September 1991",
  abstract =     "Progress in building a device capable of acquiring the
                 necessary linguistic skills for high-perforkance speech
                 synthesis is reported. Some principles and mechanisms
                 upon which such a device might be based are described.
                 Several rudimentary experiments evaluating their
                 utility are recounted.",
  keywords =     "speech processing learning systems neural networks
                 language acquisition adaptive language acquisition
                 spoken language",
}

@InProceedings{cnlp:jaravine,
  title =        "Syntactic neural network for character recognition",
  author =       "V. A. Jaravine",
  year =         "1992",
  pages =        "215--223",
  publisher =    "Int Soc for Optical Engineering",
  address =      "Bellingham, Washington",
  ISBN =         "0277-786X 0-8194-0815-8",
  booktitle =    "Machine Vision Applications in Character Recognition
                 and Industrial Inspection, San Jose, CA, February
                 1992",
  abstract =     "This article presents a synergism of syntactic 2-D
                 parsing of images and multilayered, feed- forward
                 network techniques. This approach makes it possible to
                 build a written text reading system with absolute
                 recognition rate for unambiguous text strings. The
                 Syntactic Neural Network (SNN) is created during image
                 parsing process by capturing the higher order
                 statistical structure in the ensemble of input image
                 examples. Acquired knowledge is stored in the form of
                 hierarchical image elements dictionary and syntactic
                 network. The number of hidden layers and neuron units
                 is not fixed and is determined by the structural
                 complexity of the teaching set. A proposed syntactic
                 neuron differs from conventional numerical neuron by
                 its symbolic input/output and usage of the dictionary
                 for determining the output. This approach guarantees
                 exact recognition of an image that is a combinatorial
                 variation of the images from the training set. The
                 system is taught to generalize and to make stochastic
                 parsing of distorted and shifted patterns. The
                 generalizations enables the system to perform
                 continuous incremental optimization of its work. New
                 image data learned by SNN doesn't interfere with
                 previously stored knowledge, thus leading to unlimited
                 storage capacity of the network. 8 refs.",
  keywords =     "Character recognition Neural networks Statistical
                 methods Digital image storage Image analysis Learning
                 systems Syntactic neural network Feed-forward neural
                 network Image parsing Stochastic parsing Training set
                 Local/global dictionary Written text recognition
                 Syntactic neuron",
}

@Article{cnlp:hunt,
  title =        "Neural networks for control systems - a survey",
  author =       "K. J. Hunt and D. Sbarbaro and R. Zbikowski and P. J.
                 Gawthrop",
  journal =      "Automatica",
  year =         "1992",
  volume =       "28",
  number =       "6",
  pages =        "1083--1112",
  ISBN =         "0005-1098",
  address =      "Univ of Glasgow, Glasgow, UK",
  abstract =     "This paper focuses on the promise of artificial neural
                 networks in the realm of modelling, identification and
                 control of nonlinear systems. The basic ideas and
                 techniques of artificial neural networks are presented
                 in language and notation familiar to control engineers.
                 Applications of a variety of neural network
                 architectures in control are surveyed. We explore the
                 links between the fields of control science and neural
                 networks in a unified presentation and identify key
                 areas for future research. (Author abstract) 196 Refs",
  keywords =     "Neural networks Nonlinear control systems
                 Identification (control systems) Computer architecture
                 Learning systems Predictive control systems Signal
                 filtering and prediction Cellular neural networks
                 Neuron nonlinearity Fixed point learning Trajectory
                 learning Model reference control Adaptive linear
                 control",
}

@InProceedings{cnlp:dasgupta,
  title =        "Learning capabilities of recurrent neural networks",
  author =       "B DasGupta",
  year =         "1992",
  volume =       "2",
  pages =        "822--823",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  ISBN =         "0734-7502 0-7803-0494-2",
  booktitle =    "Proceedings of the IEEE Southeastcon '92 Birmingham,
                 AL, April 1992",
  abstract =     "The author relates the power of recurrent neural
                 networks to those of other conventional models of
                 computation like Turing machines and finite automata,
                 and proves results about their learning capabilities.
                 Specifically, it is shown that (a) probabilistic
                 recurrent networks and probabilistic Turing machine
                 models are equivalent; (b) probabilistic recurrent
                 networks with bounded error probabilities are not more
                 powerful than deterministic finite automata; (c)
                 deterministic recurrent networks have the capability of
                 learning P-complete language problems; and (d)
                 restricting the weight-threshold relationship in
                 deterministic recurrent networks may allow the network
                 to learn only weaker classes of languages. 4 Refs",
  keywords =     "Neural networks Learning systems Turing machines
                 Finite automata Probability Computer programming
                 languages Recurrent neural networks Probabilistic
                 neural networks Deterministic recurrent networks",
}

@InProceedings{cnlp:oka,
  title =        "Hybrid cognitive model of conscious level processing
                 and unconscious level processing",
  author =       "N. Oka",
  year =         "1992",
  pages =        "485--490",
  publisher =    "IEEE, Piscataway, IEEE Service Center, New Jersey, USA
                 (IEEE cat n 92CH3065-0)",
  ISBN =         "0-7803-0227-3",
  address =      "Matsushita Res Inst Tokyo, Inc, Tama-ku, Kawasaki,
                 Japan",
  booktitle =    "1991 IEEE International Joint Conference on Neural
                 Networks - IJCNN '91 Singapore, Singapore",
  abstract =     "Human intelligence has been modeled in two ways:
                 modeling based on central symbolic processing, and
                 modeling based on distributed subsymbolic processing.
                 This paper points out the limitations of these two
                 approaches, and proposes a hybrid cognitive model of
                 central symbolic processing on the conscious level, and
                 distributed subsymbolic processing on the unconscious
                 level (C/U model). The advantages of the C/U model are
                 clarified by explaining various functions realized by
                 the model: multistage knowledge retrieval, recognition
                 and inference with situated knowledge, inductive
                 learning, and creative inference. Those functions are
                 realized by utilizing the close interaction between the
                 two levels. Finally, this paper describes an
                 implementation method that utilizes the characteristics
                 of a parallel logic programming language, and also
                 describes a knowledge acquisition system necessary for
                 building practical hybrid systems. 15 Refs",
  keywords =     "Cognitive systems Logic programming Cognitive models
                 Conscious level processing Unconscious level processing
                 Distributed subsymbolic processing Knowledge
                 acquisition",
}

@Article{cnlp:rocha,
  title =        "A neural net for extracting knowledge from natural
                 language data bases",
  author =       "A. F. Rocha and I. R. Guilherme and M. Theoto and A.
                 M. K. Miyadahira and M. S. Koizumi",
  journal =      "IEEE Transactions on Neural Networks",
  year =         "1992",
  volume =       "3",
  number =       "5",
  pages =        "819--828",
  ISBN =         "1045--9227",
  abstract =     "A model of a fuzzy neuron, one which increases the
                 computational power of the artificial neuron, turning
                 it also into a symbolic processing device, is
                 presented. The model proposes the synapsis to be
                 symbolically and numerically defined, by means of the
                 assignment of tokens to the presynaptic and
                 postsynaptic neurons. The matching or concatenation
                 compatibility between these tokens is used to decide
                 about the possible connections among neurons of a given
                 net. The strength of the compatible synapsis is made
                 dependent on the amount of the available presynaptic
                 and postsynaptic tokens. The symbolic and numeric
                 processing capacity of the new fuzzy neuron is used to
                 build a neural net (Jargon) to disclose the existing
                 knowledge in natural language databases such as medical
                 files, sets of interviews and reports about engineering
                 operations. 22 Refs",
  keywords =     "Neural networks Natural language processing systems
                 Artificial intelligence Codes (symbols) Fuzzy sets
                 Database systems Medical computing Knowledge extraction
                 Natural language databases Fuzzy neurons Artificial
                 neurons Symbolic processing",
}

@InProceedings{cnlp:gallant,
  title =        "A practical approach for representing context and for
                 performing word sense disambiguation using neural
                 networks.",
  author =       "S. I. Gallant",
  year =         "1992",
  pages =        "1007",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  ISBN =         "0-7803-0164-1",
  booktitle =    "International Joint Conference on Neural Networks -
                 IJCNN-91-Seattle Part 2 (of 2) Seattle, Washington 08 -
                 12 July 1991",
  abstract =     "Summary form only given. The author proposes a method
                 for representing context information so that the
                 correct meaning for a word in a sentence can be
                 selected. The approach is primarily based upon work by
                 Waltz and Pollack, who emphasized neurally plausible
                 systems. By contrast the author focuses upon
                 computationally feasible methods applicable to
                 full-scale natural language processing systems. There
                 are two key elements: a collection of context vectors
                 defined for every word used by a natural language
                 processing system, and a context algorithm that
                 computes a dynamic context vector at any position in a
                 body of text. Once the dynamic context vector has been
                 computed it is easy to choose among competing meanings
                 for a word. This choice of definitions is essentially a
                 neural network computation, and neural network learning
                 algorithms should be able to improve the system's
                 choices. Good candidates for full-scale context vector
                 implementations are machine translation systems and
                 Japanese word processors.",
  keywords =     "neural networks - applications learning systems
                 computer programming - algorithms dynamics data
                 processing - word processing japanese word processors
                 summary only",
}

@InProceedings{cnlp:iooss,
  title =        "From lattices of phonemes to sentences: {A} recurrent
                 neural network approach.",
  author =       "C. Iooss",
  year =         "1992",
  pages =        "833--838",
  publisher =    "IEEE",
  address =      "IEEE Service Center, Piscataway, NJ, USA.",
  ISBN =         "0-7803-0164-1",
  booktitle =    "International Joint Conference on Neural Networks -
                 IJCNN-91-Seattle Part 2 (of 2) Seattle, Washington,
                 8--12 July 1991",
  abstract =     "The author presents preliminary investigations
                 concerning the use of a sequential neural network for
                 lexical decoding in continuous speech recognition. They
                 explore the architecture introduced by J.L. Elman
                 (Center for Research in Language Technical Report 8801,
                 UCSD, April 1988) for predicting successive elements of
                 a sequence. This recurrent network admits sequential
                 inputs. This model is based on a multilayer
                 architecture and contains special units, called context
                 units, sensitive to the recent activation history of
                 the network. It is suggested that this model be used
                 for lexical decoding in continuous speech recognition.
                 For that purpose, an extension of Elman's model is
                 presented in order to treat erroneous sequential inputs
                 and in order to label patterns. It is suggested that
                 the context units be updated considering their previous
                 values and not only the values of the hidden units.
                 Moreover, output units represent words instead of the
                 prediction on the next phoneme. Preliminary
                 experimental results are given. 15 Refs.",
  keywords =     "Speech - Recognition Neural Networks - Applications
                 Automata Theory - Computational Linguistics Voice
                 Recognition Recurrent Neural Networks Lexical
                 Decoding",
}

@InProceedings{cnlp:gera,
  title =        "Netclass--{A} fresh look at connectionist category
                 formation.",
  author =       "M. H. Gera",
  year =         "1992",
  pages =        "225--230",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  ISBN =         "0-7803-0164-1",
  booktitle =    "International Joint Conference on Neural Networks -
                 IJCNN-91-Seattle Part 2 (of 2) Seattle, Washington,
                 8--12 July 1991",
  abstract =     "The author describes Netclass, a proposed neural
                 solution to the problem ofcategory formation and
                 representation. Superordinates in Netclass have a
                 fundamentally disjunctive representation. They are also
                 strongly related to the actual scenes in which they are
                 grouped. It is shown how these features, along with
                 Netclass' connectionist nature, give a better account
                 of some of the more recent data on superordinate
                 categories than that offered by existing categorization
                 models. Disjunction turns out to be of use in
                 representing components. It is suggested that this
                 offers a solution to a problem inherent in neural net
                 concept component representation. 15 Refs.",
  keywords =     "systems science and cybernetics - cognitive systems
                 neural networks computer vision artificial intelligence
                 learning systems artificial neural networks symbolic
                 processing artificial intelligence models machine
                 vision psychology",
}

@InProceedings{cnlp:roques,
  title =        "Strategies of unsupervised learning for a parallel
                 parsing architecture.",
  author =       "M. Roques and D. Beroule",
  year =         "1992",
  pages =        "215--218",
  publisher =    "IEEE, IEEE Service Center, Piscataway, NJ, USA.",
  ISBN =         "0-7803-0164-1",
  booktitle =    "International Joint Conference on Neural Networks -
                 IJCNN-91-Seattle Part 2 (of 2) Seattle, Washington",
  abstract =     "A study concerning the analysis of natural language is
                 reported, involving a serial and parallel processing
                 principle already applied to pattern recognition tasks:
                 guided propagation. The network of processing units
                 which supports this principle is connected in the
                 course of processing, thanks to basic differentiation
                 and generalization mechanisms. It is shown how these
                 basic learning mechanisms can be adapted so as to serve
                 two purposes: the automatic clustering of unexpected
                 words and the extraction of lexical substructures from
                 examples of sentences through different possible
                 strategies. Compared with classical natural language
                 parsers the network that results from the learning
                 mechanisms presented here implements a deterministic,
                 left-to-right, breadth-first parser. The system follows
                 every path simultaneously, in synchrony with the input
                 flow. It works bottom-up from the input data, with a
                 top-down trend, as pathways are reinforced. At a given
                 time, part of the network tends to work in a top-down
                 mode, while the other part works bottom-up, depending
                 selectively on the 'familiarity' of the stored items. 4
                 Refs.",
  keywords =     "Learning Systems - Analysis Systems Science AND
                 Cybernetics - Man Machine Systems Natural Language
                 Processing Systems Neural Networks Computer Systems,
                 Digital - Parallel Processing Machine Learning Parsing
                 Artificial Neural Networks Natural Language",
}

@InProceedings{cnlp:mcmillan,
  title =        "Learning explicit rules in a neural network.",
  author =       "C. McMillan and M. C. Mozer and P Smolensky",
  year =         "1992",
  pages =        "83--88",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  ISBN =         "0-7803-0164-1",
  booktitle =    "International Joint Conference on Neural Networks -
                 IJCNN-91-Seattle Part 2 (of 2) Seattle, Washington,
                 8--12 July 1991",
  abstract =     "The authors propose an architecture called RuleNet,
                 which, based on knowledge of the task domain, allows
                 for the extraction of symbolic condition-action rules
                 from the connection strengths in a neural net. By
                 exploiting constraints inherent in the domain of
                 symbolic string to string mappings, RuleNet can learn
                 to induce explicit, symbolic, condition-action rules
                 from examples. These rules represent a powerful
                 representational language with which to describe the
                 inner workings of a network. In addition, they
                 facilitate faster learning and generalize perfectly to
                 further examples that follow the rules. This formal
                 string manipulation task can be viewed as an
                 abstraction of several interesting cognitive models in
                 the connectionist literature, such as case role
                 assignment or translating English text into phonetic
                 symbols. 7 Refs.",
  keywords =     "neural networks - analysis learning systems systems
                 science and cybernetics - cognitive systems artificial
                 neural networks machine learning cognitive models
                 symbolic processing rule based systems",
}

@InProceedings{cnlp:gorse,
  title =        "Learning sequential structure with recurrent p{RAM}
                 nets.",
  author =       "D. Gorse and J. G. Taylor",
  year =         "1992",
  pages =        "37--42",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  ISBN =         "0-7803-0164-1",
  booktitle =    "International Joint Conference on Neural Networks -
                 IJCNN-91-Seattle Part 2 (of 2) Seattle, WA, 08--12 July
                 1991",
  abstract =     "Networks of probabilistic RAMs (pRAMs) may be trained
                 using both gradient descent and reinforcement training
                 rules. These two approaches are applied to the problem
                 of learning a simple grammar from exposure to a finite
                 set of grammatically correct strings, and it is seen
                 that the combination of nonlinearity and stochasticity
                 in the pRAM output functions enables a recurrent
                 network to learn the grammar quickly and accurately.
                 Both gradient descent and reinforcement training can be
                 used to train a pRAM net to recognize a simple regular
                 grammar (dual parity) with a speed which greatly
                 exceeds that which can be achieved with networks of
                 more conventional processor. In particular, it was
                 demonstrated that the use of the stochastic features of
                 the pRAM in reinforcement training would lead to a very
                 significant reduction in training time if the system
                 were implemented in hardware. 7 Refs.",
  keywords =     "Learning Systems - Analysis Data Storage, Digital -
                 Random Access Neural Networks Automata Theory -
                 Grammars Mathematical Techniques - Algorithms
                 Artificial Neural Networks Learning Algorithms
                 Probabilistic Ram Neural Hardware Grammar Learning
                 Language Learning",
}

@InProceedings{cnlp:muthusamy,
  title =        "A segment-based approach to automatic language
                 identification.",
  author =       "Y. K. Muthusamy and R. A. Cole and M. Gopalakrishnan",
  year =         "1991",
  volume =       "1",
  pages =        "353--356",
  publisher =    "IEEE, IEEE Service Center, Piscataway, NJ, USA (IEEE
                 cat n 91CH2977-7).",
  ISBN =         "0736-7791 0-7803-003-3",
  booktitle =    "Proceedings of the 1991 International Conference on
                 Acoustics, Speech, and Signal Processing - Icassp 91
                 Toronto, Ont, Canada",
  abstract =     "A segment-based approach to automatic language
                 identification is discussed which is based on the idea
                 that the acoustic structure of languages can be
                 estimated by segmenting speech into broad phonetic
                 categories. Automatic language identification can then
                 be achieved by computing features that describe the
                 phonetic and prosodic characteristics of the language,
                 and using these feature measurements to train a
                 classifier to distinguish between languages. As a first
                 step in this approach, a multilanguage,
                 neural-network-based segmentation and broad
                 classification algorithm using seven broad phonetic
                 categories has been built. The algorithm was trained
                 and tested on separate sets of speakers of American
                 English, Japanese, Mandarin Chinese, and Tamil. It
                 currently performs with an accuracy of 82.3% on the
                 utterances of the test set. 7 Refs.",
  keywords =     "Speech - Recognition Database Systems Computer
                 Programming - Algorithms Automatic Language
                 Identification Phonetic Characteristics Prosodic
                 Characteristics",
}

@Article{cnlp:hilberg,
  title =        "The network of human languages and its technological
                 implementation.",
  author =       "W. Hilberg",
  journal =      "Frequenz",
  year =         "1991",
  volume =       "45",
  number =       "11--12",
  pages =        "275--284",
  ISBN =         "0016-1136",
  address =      "Technischen Hochschule Darmstadt, Germany",
  abstract =     "A new class of networks is presented, which was
                 derived from text structure analysis. These networks
                 exhibit remarkable differences with symmetrical
                 networks (e.g. hypercubes) as well as with neural
                 networks. Its structure is defined generally by
                 statistical rules. Diameter and mean diameter are both
                 rather small, and the number of connections per node is
                 low, i.e. these networks have favourable properties for
                 technical applications, especially in areas where
                 application specific solutions are desired. The
                 generation of natural languages might be a typical
                 problem of this kind. A great variety of individual
                 network structures is possible (comparable with the
                 number of individual languages), including structures
                 which are so regular, that a random origin can not be
                 supposed. (author abstract) 9 Refs. In German.",
  keywords =     "Neural Networks Computer Simulation - Applications
                 Statistical Methods - Applications Human Language
                 Network TEXT Structure Analysis Natural Language
                 Generation",
}

@InProceedings{cnlp:cawley,
  title =        "Application of neural networks to cognitive phonetic
                 modelling.",
  author =       "G. C. Cawley and A. D. P. Green",
  year =         "1991",
  number =       "349",
  pages =        "280--284",
  publisher =    "IEE",
  address =      "Stevenage, UK",
  ISBN =         "0537-9987",
  booktitle =    "2nd International Conference on Artificial Neural
                 Networks, Bournemouth, UK, November 1991",
  abstract =     "A neural network is used to generate control
                 parameters for a parallel formant speech synthesizer,
                 corresponding to a sequence of allophonic tokens.
                 Training is to be accomplished using formant data
                 obtained from both natural and synthetic speech. It is
                 intended that theories of cognitive phonetics,
                 currently being developed in the Department of Language
                 and Linguistics at the University of Essex, will be
                 used in order to improve the modelling of
                 coarticulation. (author abstract) 8 Refs.",
  keywords =     "Neural Networks - Applications Speech - Synthesis
                 Cognitive Phonetic Modelling",
}

@InProceedings{cnlp:ludermir,
  title =        "Logical neural nets and distributed implementations of
                 weighted regular languages.",
  author =       "T. B. Ludermir",
  year =         "1991",
  number =       "349",
  pages =        "158--162",
  publisher =    "IEE",
  addresss =     "Stevenage, UK",
  ISBN =         "0537-9987",
  booktitle =    "2nd International Conference on Artificial Neural
                 Networks, Bournemouth, UK, November 1991",
  abstract =     "A logical neural network, Aleksander (1), is a finite
                 state machine then it is only possible to recognise
                 regular grammars with these networks. When extra memory
                 is associated with the nodes of these networks, the
                 computational power of the model is increased and now
                 weighted regular grammars, Salomaa (14), can be
                 recognised. Through a constructive method based on the
                 complexity of the production rules of the grammar, a
                 logical network can be built to recognise any weighted
                 regular language. The network generated by the
                 constructive method is a distributed implementation of
                 the weighted regular language. (author abstract) 14
                 Refs.",
  keywords =     "neural networks automata theory - formal languages
                 logical neural networks",
}

@InProceedings{cnlp:mueller,
  title =        "Connectionist natural language parsing with
                 Brain{C}.",
  author =       "A. Mueller and A. Zell",
  year =         "1991",
  pages =        "188--196",
  publisher =    "Int Soc for Optical Engineering, Bellingham, WA,
                 USA.",
  ISBN =         "0277-786X 0-8194-0578-7",
  address =      "Univ. Stuttgart, 1 Stuttgart 80, Federal Republic of
                 Germany",
  booktitle =    "Applications of Artificial Neural Networks II Orlando,
                 Florida",
  abstract =     "A close examination of pure neural parsers shows that
                 they either could not guarantee the correctness of
                 their derivations or had to hard-code seriality into
                 the structure of the net. The authors therefore decided
                 to use a hybrid architecture, consisting of a serial
                 parsing algorithm and a trainable net. The system
                 fulfills the following design goals: (1) parsing of
                 sentences without length restriction, (2) soundness and
                 completeness for any context-free language, and (3)
                 learning the applicability of parsing rules with a
                 neural network to increase the efficiency of the whole
                 system. BrainC (backtracktacking and backpropagation in
                 C) combines the well- known shift-reduce parsing
                 technique with backtracking with a backpropagation
                 network to learn and represent typical structures of
                 the trained natural language grammars. The system has
                 been implemented as a subsystem of the Rochester
                 Connectionist Simulator (RCS) on SUN workstations and
                 was tested with several grammars for English and
                 German. The design of the system and then the results
                 are discussed. 7 Refs.",
  keywords =     "Natural Language Processing Systems - Analysis
                 Learning Systems Neural Networks Automata Theory -
                 Computational Linguistics Artificial Neural Networks
                 Parsing BACK Propagation Learning",
}

@InProceedings{cnlp:figuero,
  title =        "Information representation analysis in a neural
                 network.",
  author =       "J. FigueroaNazuno and G. PerezElizalde and E.
                 VargasMedina and M. G. RaggiGonzalez",
  year =         "1991",
  pages =        "2241--2246",
  publisher =    "IEEE, IEEE Service Center, Piscataway, NJ, USA (IEEE
                 cat n 91CH3065-0).",
  ISBN =         "0-7803-0227-3",
  booktitle =    "1991 IEEE International Joint Conference on Neural
                 Networks - IJCNN '91, Singapore, November 1991",
  abstract =     "The authors study the mathematical behavior of the
                 hidden layer of a generalized delta rule type neural
                 network (GDR) by analyzing the weights and thresholds
                 in the network, when it learned and didn't learn, in a
                 typical situation in neurocomputation. The GDR was used
                 in a C language program. There are three representation
                 hypotheses: a) the local, which states that information
                 encoding takes place in local parts of the network; b)
                 the generalized, which states that information is
                 located in extended areas in the network; and c) the
                 global, which states that total behavior represents the
                 information in the networks. Several intensive
                 computations were carried out to analyze the neural
                 network internal behavior in situations where it did
                 and didn't learn. The information shows clearly that
                 representation as a global behavior in the hidden layer
                 is responsible for learning, and not local behavior
                 situations. 15 Refs.",
  keywords =     "Neural Networks - Theory Computer Programming
                 Languages - C Multilayer Neural Networks Hidden Layer
                 Generalized Delta RULE Neural Network Information
                 Representation Analysis",
}

@Article{cnlp:wu92,
  title =        "Neural network feature maps for Chinese phonemes.",
  author =       "P. Wu and K. Warwick and M. Koska",
  journal =      "Neurocomputing",
  year =         "1992",
  volume =       "4",
  number =       "1--2",
  pages =        "109--112",
  ISBN =         "0925-2312",
  address =      "Univ of Reading, UK",
  abstract =     "It has been shown through a number of experiments that
                 neural networks can be used for a phonetic typewriter.
                 Algorithms can be looked on as producing
                 self-organizing feature maps which correspond to
                 phonemes. In the Chinese language the utterance of a
                 Chinese character consists of a very simple string of
                 Chinese phoneme. With this as a starting point, a
                 neural network feature map for Chinese phonemes can be
                 built up. In this paper, feature map structures for
                 Chinese phonemes are discussed and tested. This
                 research on a Chinese phonetic feature map is important
                 both for Chinese speech recognition and for building a
                 Chinese phonetic typewriter. (Author abstract)",
  keywords =     "Neural Networks - Applications Speech - Recognition
                 Computer Programming - Algorithms Chinese Phonemes
                 Feature MAPS",
}

@InProceedings{cnlp:mueller91,
  title =        "Natural language parsing in a hybrid
                 connectionist-symbolic architecture.",
  author =       "A. Mueller and A. Zell",
  year =         "1991",
  number =       "Pt2",
  pages =        "875--881",
  publisher =    "Int Soc for Optical Engineering, Bellingham, WA,
                 USA.",
  ISBN =         "0277-786X",
  address =      "Universitat Stuttgart, Stuttgart, Germany",
  booktitle =    "Applications of Artificial Intelligence IX Orlando,
                 Florida",
  abstract =     "We here present a hybrid symbolic connectionist
                 parser, which was designed to fulfill the following
                 goals: 1) parsing of sentences without length
                 restriction, 2) soundness and completeness for any
                 context-free grammar, and 3) learning the applicability
                 of parsing rules with a neural network. Our hybrid
                 architecture consists of a serial parsing algorithm and
                 a trainable net. BrainC (Backtracking and
                 Backpropagation in C) combines the well known
                 shift-reduce parsing technique with backtracking with a
                 backpropagation network to learn and represent the
                 typical properties of the trained natural language
                 grammars. The system has been implemented as a
                 subsystem of the Rochester Connectionist Simulator
                 (RCS) on SUN-Workstations and was tested with several
                 grammars for English and German. We discuss how brainC
                 reached its design goals and what results we observed.
                 (Edited author abstract) 7 Refs.",
  keywords =     "Natural Language Processing Systems - Computer
                 Applications Computer Programming - Algorithms Computer
                 Architecture Neural Networks Connectionist Parsers
                 Backpropagation Networks",
}

@InProceedings{cnlp:yin,
  title =        "An associative memory model of language.",
  author =       "H. F. Yin and J. W. Tai",
  year =         "1990",
  pages =        "663--670",
  publisher =    "IEEE, IEEE Service Center, Piscataway, NJ, USA",
  address =      "Inst of Autom, Academia Sinica, Beijing, China",
  booktitle =    "1990 International Joint Conference on Neural Networks
                 - IJCNN 90 Part 3 (of 3) San Diego, California",
  abstract =     "An associative-memory model of language based on a
                 neural network is proposed. It is shown that a language
                 with finite sentences can be stored in a neural net
                 completely. The memorizing process is realized by a
                 dynamical learning algorithm which is convergent. Thus,
                 a neural net not only has the ability to memorize
                 syntactic information but can also memorize the
                 semantic information of a language. The model is
                 similar to human memory in some respects. A set of
                 English words and Chinese sentences has been tested,
                 and the simulation results on recognizing a word in
                 default of a few characters are given. 2 Refs.",
  keywords =     "DATA Storage, Digital - Associative Neural Networks
                 Automata Theory Character Recognition Semantics Syntax
                 Chinese Sentences English Words",
}

@InProceedings{cnlp:peschl,
  title =        "A cognitive model coming up to epistemological claims:
                 Constructivist aspects to modeling cognition.",
  author =       "M. F. Peschl",
  year =         "1990",
  pages =        "657--662",
  publisher =    "IEEE, IEEE Service Center, Piscataway, NJ, USA (IEEE
                 cat n 90CH2879-5).",
  address =      "Dept for Epistemol \& Cognitive Sci, Univ of Vienna,
                 Wien, Austria",
  booktitle =    "1990 International Joint Conference on Neural Networks
                 - IJCNN 90 Part 3 (of 3) San Diego, California",
  abstract =     "An alternative approach to modeling cognition is
                 discussed which considersnot only computer science, PDP
                 (parallel distributed processing), and neuroscience
                 aspects, but also epistemological issues. The aim is
                 the construction of an adequate cognitive model
                 satisfying epistemological as well as neuroscience
                 claims. This project considers a constructivist point
                 of view, implying that one must rethink the notions of
                 knowledge representation, language, communication, etc.
                 The proposed cognitive model is directly coupled to its
                 environment; that is, there is only physical
                 interaction, and no symbolic instance exists in
                 between. This cognitive system is provided with several
                 sensors and effectors allowing structural coupling to
                 the environment (i.e., moving around, perceiving,
                 emitting signals, etc.). Computer graphics methods are
                 being used to model the retina. This approach seems to
                 be better than the approaches taken by, for example,
                 orthodox AI, because one is trying to understand and
                 model the phenomenon of cognition from a bottom-up
                 point of view, grasping basic processes first and only
                 then simulating language, communications, etc. 15
                 Refs.",
  keywords =     "SYSTEMS SCIENCE AND CYBERNETICS - Cognitive Systems
                 AUTOMATA THEORY NEURAL NETWORKS COMPUTER GRAPHICS
                 ARTIFICIAL INTELLIGENCE COGNITIVE MODELS EPISTEMOLOGY
                 NEUROSCIENCE",
}

@Article{cnlp:miller,
  title =        "Representing and computing regular languages on
                 massively parallel networks.",
  author =       "M. I. Miller and B. Roysam and K. R. Smith and J. A.
                 O'Sullivan",
  journal =      "IEEE Transactions on Neural Networks",
  year =         "1991",
  volume =       "2",
  number =       "1",
  pages =        "56--72",
  ISBN =         "1045--9227",
  address =      "Dept of Electr Eng, Washington Univ, St Louis,
                 Missouri",
  abstract =     "A general method is proposed for incorporating
                 rule-based constraints corresponding to regular
                 languages into stochastic inference problems, thereby
                 allowing for a unified representation of stochastic and
                 syntactic pattern constraints. The authors' approach
                 establishes the formal connection of rules to Chomsky
                 grammars and generalizes the original work of Shannon
                 on the encoding of rule-based channel sequences to
                 Markov chains of maximum entropy. This maximum entropy
                 probabilistic view leads to Gibbs representations with
                 potentials which have their number of minima growing at
                 precisely the exponential rate that the language of
                 deterministically constrained sequences grow. These
                 representations are coupled to stochastic diffusion
                 algorithms, which sample the language-constrained
                 sequences by visiting the energy minima according to
                 the underlying Gibbs probability law. The coupling to
                 stochastic search methods yields the all-important
                 practical result that fully parallel stochastic
                 cellular automata can be derived to generate samples
                 from the rule-based constraint sets. The production
                 rules and neighborhood state structure of the language
                 of sequences directly determine the necessary
                 connection structures of the required parallel
                 computing surface. Representations of this type have
                 been mapped to the DAP-510 massively parallel processor
                 consisting of 1024 mesh-connected bit-serial processing
                 elements for performing automated segmentation of
                 electron-micrograph images. 66 Refs.",
  keywords =     "NEURAL NETWORKS AUTOMATA THEORY - Formal Languages
                 Probability - Random Processes IMAGE PROCESSING
                 COMPUTER PROGRAMMING - Algorithms MASSIVELY PARALLEL
                 NETWORKS RULE BASED CONSTRAINTS STOCHASTIC INFERENCE
                 PROBLEMS",
}

@Article{cnlp:stjohn,
  title =        "Learning and applying contextual constraints in
                 sentence comprehension.",
  author =       "M. F. StJohn and J. L. McClelland",
  journal =      "Artificial Intelligence",
  year =         "1990",
  volume =       "46",
  number =       "1--2",
  pages =        "217--257",
  ISBN =         "0004-3702",
  address =      "Carnegie-Mellon Univ, Pittsburgh, Pennsylvania",
  abstract =     "A parallel distributed processing model is described
                 that learns to comprehend single clause sentences.
                 Specifically, it assigns thermatic roles to sentence
                 constituents, disambiguates ambiguous words,
                 intantiates vague words, and elaborates implied roles.
                 The sentences are pre-segmented into constituent
                 phrases. Each constituent is processed in turn to
                 update an evolving representation of the event
                 described by the sentence. The model uses the
                 information derived from from each constituent to
                 revise its ongoing interpretation of the sentence and
                 to anticipate additional constituents. The network
                 learns to perform these tasks through practice on
                 processing example sentence/event pairs. The learning
                 procedure allows the model to take a statistical
                 approach to solving the bootstrapping problem of
                 learning the syntax and semantics of a language from
                 the same data. The model performs very well on the
                 corpus of sentences on which it was trained, and
                 generalizes to sentences on which it was not trained,
                 but learns slowly. (author abstract) 35 Refs.",
  keywords =     "SPEECH - Processing NEURAL NETWORKS LEARNING SYSTEMS
                 SPEECH UNDERSTANDING CONNECTIONIST NETWORKS",
}

@Article{cnlp:derthick,
  title =        "Mundane reasoning by settling on a plausible model.",
  author =       "M. Derthick",
  journal =      "Artificial Intelligence",
  year =         "1990",
  volume =       "46",
  number =       "1--2",
  pages =        "107--157",
  ISBN =         "0004-3702",
  address =      "MCC, Austin, Texas",
  abstract =     "Connectionist networks are well suited to everyday
                 common sense reasoning.Their ability to simultaneously
                 satisfy multiple soft constraints allows them to select
                 from conflicting information in finding a plausible
                 interpretation of a situation. However these networks
                 are poor at reasoning using the standard semantics of
                 classical logic, based on truth in all possible models.
                 This article shows that using an alternate semantics,
                 based on truth in a single most plausible model, there
                 is an elegant mapping from theories expressed using the
                 syntax of propositional logic onto connectionist
                 networks. An extension of this mapping to allow for
                 limited use of quantifiers suffices to build a network
                 from knowledge bases expressed in a frame language
                 similar to KL-ONE. Although finding optimal models of
                 these theories is intractable, the networks admit a
                 hill climbing search algorithm that can be turned to
                 give satisfactory answers in familiar situations. The
                 article concludes with an example of retrieval
                 involving incomplete and inconsistent information.
                 Although this example works well, much remains before
                 realistic domains are feasible. (Author abstract) 60
                 Refs.",
  keywords =     "NEURAL NETWORKS COMPUTER METATHEORY - Formal Logic
                 CONNECTIONIST NETWORKS KNOWLEDGE BASED SYSTEMS
                 PROPOSITIONAL LOGIC REASONING",
}

@InProceedings{cnlp:gorin90,
  title =        "On adaptive acquisition of language.",
  author =       "A. L. Gorin and S. E. Levinson and L. G. Miller and A.
                 N. Gertner and A. Ljolje and E. R. Goldman",
  year =         "1990",
  volume =       "1",
  pages =        "601--604",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  ISBN =         "0736-7791",
  booktitle =    "Proceedings of the 1990 International Conference on
                 Acoustics, Speech, and Signal Processing - ICASSP 90
                 Albuquerque, NM, April 1990",
  abstract =     "A system that automatically acquires a language model
                 for a particular task from semantic-level information
                 is described. This is in contrast to systems with
                 predefined vocabulary and syntax. The purpose of the
                 system is to map spoken or typed input into a machine
                 action. To accomplish this task a medium-grain neural
                 network is used. An adaptive training procedure is
                 introduced for estimating the connection weights. It
                 has the advantages of rapid, single-pass and
                 order-invariant learning. The resulting weights have
                 information-theoretic significance and do not require
                 gradient search techniques for their estimation. The
                 system was experimentally evaluated on three text-based
                 tasks: a three-class inward-call manager with an
                 acquired vocabulary of over 1600 words, a 15-action
                 subset of the DARPA Resource Manager with an acquired
                 vocabulary of over 700 words, and discrimination
                 between idiomatic phrases meaning yes or no. 14 Refs.",
  keywords =     "SPEECH - Recognition CONTROL SYSTEMS, ADAPTIVE -
                 Estimation LEARNING SYSTEMS Database SYSTEMS NEURAL
                 NETWORKS DARPA RESOURCE MANAGER LANGUAGE MODELS",
}

@InProceedings{cnlp:jain90b,
  title =        "Robust connectionist parsing of spoken language.",
  author =       "A. N. Jain and A. H. Waibel",
  year =         "1990",
  volume =       "1",
  pages =        "593--596",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  ISBN =         "0736-7791",
  address =      "Sch of Comput Sci, Carnegie Mellon Univ, Pittsburgh,
                 Pennsylvania",
  booktitle =    "Proceedings of the 1990 International Conference on
                 Acoustics, Speech, and Signal Processing - ICASSP 90
                 Albuquerque, New Mexico, April 1990",
  abstract =     "A modular, recurrent connectionist network
                 architecture which learns to robustly perform
                 incremental parsing of complex sentences is presented.
                 From sequential input, one word at a time, the networks
                 learn to do semantic role assignment, noun phrase
                 attachment, and clause structure recognition for
                 sentences with passive constructions and center
                 embedded clauses. The networks make syntactic and
                 semantic predictions at every point in time, and
                 previous predictions are revised as expectations are
                 affirmed or violated with the arrival of new
                 information. The networks induce their own grammar
                 rules for dynamically transforming an input sequence of
                 words into a syntactic/semantic interpretation. These
                 networks generalize and display tolerance to input
                 which has been corrupted in ways common in spoken
                 language. 7 Refs.",
  keywords =     "speech - recognition learning systems control systems
                 - robustness automata theory - grammars robust
                 connectionist parsing",
}

@InProceedings{cnlp:kim,
  title =        "A study on the recognition of the Korean monothongs
                 using artificial neural net models.",
  author =       "K. Kim and I. Kim and H. Hwang",
  year =         "1990",
  pages =        "364--371",
  publisher =    "IEEE Computer Society Press",
  address =      "Los Alamitos, CA",
  booktitle =    "Proceedings of the 5th Jerusalem Conference on
                 Information Technology, Jerusalem, Israel, October
                 1990",
  abstract =     "The implementation and comparison of various
                 artificial neural network models for recognition of
                 Korean monothongs are reported. The goal is to develop
                 an intelligent speech-based man-machine computer
                 interface. The neural networks used were the multilayer
                 perceptron, the time-delay neural net, the
                 self-organizing feature map, and the interactive and
                 competitive model. These four models were compared with
                 respect to recognition rate and learning speed under
                 various conditions. Experiments taking context effects,
                 the most important problem in recognizing phonemes from
                 continuous speech, into consideration were also
                 performed. The models showed 90%-96% recognition rate
                 for a male speaker. A strategy for Korean speech
                 recognition using artificial neural networks has been
                 developed on the basis of these results. 18 Refs.",
  keywords =     "NEURAL NETWORKS - Applications NATURAL LANGUAGE
                 PROCESSING SYSTEMS COMPUTATIONAL MODELS",
}

@Article{cnlp:minami,
  title =        "Large-vocabulary spoken word recognition using
                 time-delay neural network, phoneme spotting and
                 predictive {LR}-parsing.",
  author =       "Y. Minami and H. Sawai and M. Miyatake",
  journal =      "Systems and Computers in Japan",
  year =         "1991",
  volume =       "22",
  number =       "1",
  pages =        "99--108",
  ISBN =         "0882-1666",
  address =      "Keio Univ, Yokohama, Japan",
  abstract =     "This paper proposes a large-vocabulary speech
                 recognition system using a phoneme spotting method by a
                 time-delay neural network (TDNN) and a predictive LR
                 parser. This is the first attempt to recognize large
                 vocabulary speech using neural networks. The prediction
                 of phonemes in words is performed by a predictive LR
                 parser. Time alignment between predicted phonemes by
                 the predicted LR parser and phoneme spotting results
                 via TDNN is realized using a DTW (dynamic time warping)
                 method. Speaker-dependent recognition for a 5240-word
                 vocabulary using 2620 test words uttered by a male
                 announcer resulted in a rate of 92.6 percent for the
                 top choices, rates of 97.6 and 99.1 percent for the
                 second and fifth choices, respectively. (author
                 abstract)",
  keywords =     "SPEECH - Recognition NEURAL NETWORKS - Applications
                 TIME-DELAY NEURAL NETWORKS (TDNN) DYNAMIC TIME WARPING
                 (DTW) PHONEME RECOGNITION",
}

@InProceedings{cnlp:namatae,
  title =        "Connectionist learning with high-order functional
                 networks and its internal representation.",
  author =       "A Namatame",
  year =         "1989",
  pages =        "542--547",
  publisher =    "IEEE, IEEE Service Center, Piscataway, NJ, USA.
                 Available from IEEE Service Cent, Piscataway, NJ,
                 USA.",
  ISBN =         "0-8186-1984-8",
  address =      "Natl Defense Acad, Dep of Comput Sci, Yokosuka,
                 Japan",
  booktitle =    "IEEE International Workshop on Tools for Artificial
                 Intelligence: Architectures, Languages and Algorithms
                 Fairfax, Virginia",
  abstract =     "A novel architecture for supervised neural network
                 learning is proposed.The necessary conditions of the
                 network architecture for learning the structures of
                 continuous mappings are obtained. The novel network
                 architecture comprises high-order functional networks
                 with some high-order functional units as input units.
                 It is shown that high-order functional networks trained
                 with backpropagation can generalize and infer the
                 highly nonlinear structures of the continuous mappings.
                 The internal representation capability of the
                 high-order functional networks is analyzed. Nonlinear
                 mappings can be characterized by the features of their
                 extrema and curvatures. It is shown that the
                 combination of the high-order functional input units
                 and the hidden units makes it possible to realize and
                 learn a proper internal representation of the networks
                 for extracting these features of the continuous
                 mappings. On the basis of these internal representation
                 capabilities, a methodology for determining the network
                 architecture and parameters is proposed. 14 Refs.",
  keywords =     "SYSTEMS SCIENCE AND CYBERNETICS - Learning Systems
                 COMPUTER SYSTEMS, DIGITAL - Parallel Processing
                 ARTIFICIAL INTELLIGENCE CONNECTIONIST LEARNING
                 SUPERVISED NEURAL NETWORK LEARNING NONLINEAR MAPPINGS
                 CONTINUOUS MAPPINGS",
}

@InProceedings{cnlp:lange89,
  title =        "Phase-locking of artificial neural oscillators can
                 perform dynamic role-binding and inferencing.",
  author =       "T. E. Lange and J. J. Vidal and M. G. Dyer",
  year =         "1989",
  pages =        "595",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  booktitle =    "IJCNN International Joint Conference on Neural
                 Networks, Washington, DC, June 1989",
  abstract =     "Summary form only given, as follows. Previously, the
                 authors had described a localist spreading-activation
                 model, ROBIN (role binding and inferencing network),
                 which uses stable, uniquely-identifying activation
                 patterns, called signatures, to represent the dynamic
                 role-bindings critical for high-level natural language
                 understanding tasks. A unique signature activation on a
                 node represents a role binding, which can be propagated
                 as activation across long paths of nodes to allow
                 inferencing. The authors illustrate that metastable
                 artificial neural oscillators can be used to implement
                 signature activations. In this novel model, groups of
                 relaxation oscillators with unique patterns of natural
                 oscillation frequencies serve as signatures.
                 Phase-locking of gated, interacting oscillators allows
                 the signatures to be propagated dynamically across the
                 network for inferencing. Paths of synchronized
                 oscillators form a chain of role bindings representing
                 the model's plan/goal analysis of its natural language
                 input.",
  keywords =     "systems science and cybernetics - neural nets phase
                 locked loops artificial neural oscillators natural
                 language understanding tasks relaxation oscillators
                 role binding signature activations",
}

@InProceedings{cnlp:samad,
  title =        "Hybrid distributed/local connectionist
                 architectures.",
  author =       "T Samad",
  year =         "1989",
  pages =        "583",
  publisher =    "IEEE, IEEE Service Center, Piscataway, NJ, USA.
                 Available from IEEE Service Cent (cat n 89CH2765-6),
                 Piscataway, NJ, USA.",
  address =      "Honeywell, Golden Valley, Minnesota",
  booktitle =    "IJCNN International Joint Conference on Neural
                 Networks Washington, DC",
  abstract =     "Summary form only given, as follows. A class of
                 neural-network architectures is described that uses
                 both distributed and local representation. The
                 distributed representations are used for input and
                 output, thereby enabling associative, noise-tolerant
                 interaction with the environment. Internally, all
                 representations are fully local. This simplifies weight
                 assignment and makes the networks easy to configure for
                 specific applications. These hybrid distributed/local
                 architectures are especially useful for applications
                 were structured information needs to be represented.
                 Three such applications are briefly discussed: a scheme
                 for knowledge representation, a connectionist
                 rule-based system, and a knowledge-base browser.",
  keywords =     "SYSTEMS SCIENCE AND CYBERNETICS - Neural Nets COMPUTER
                 SYSTEMS, DIGITAL - Distributed ARTIFICIAL INTELLIGENCE
                 DISTRIBUTED/LOCAL CONNECTIONIST ARCHITECTURES
                 CONNECTIONIST RULE-BASED SYSTEM KNOWLEDGE-BASE BROWSER
                 KNOWLEDGE REPRESENTATION",
}

@InProceedings{cnlp:lee,
  title =        "Learning distributed representations of conceptual
                 knowledge.",
  author =       "G. Lee and M. Flowers and M. Dyer",
  year =         "1989",
  pages =        "582",
  publisher =    "IEEE",
  addresss =     "Piscataway, NJ",
  booktitle =    "IJCNN International Joint Conference on Neural
                 Networks Washington, DC, June 1989",
  abstract =     "Summary form only given, as follows. The authors argue
                 that distributed representations must satisfy five
                 criteria in order to serve as an adequate foundation
                 for constructing and manipulating conceptual knowledge.
                 These criteria are: automaticity, portability,
                 structure encoding, semantic microcontent, and
                 convergence. In our approach, distributed
                 representations of semantic relations (i.e.,
                 propositions) are formed by recirculating the hidden
                 layer in recurrent parallel distributed processing
                 networks. The authors' experiments show that the
                 resulting distributed semantic representations (DSRs)
                 satisfy all of the above criteria. They believe that
                 DSRs can help supply an important building block in
                 developing more complex connectionist architectures for
                 higher level inferencing, as is required in natural
                 language processing.",
  keywords =     "SYSTEMS SCIENCE AND CYBERNETICS - Neural Nets COMPUTER
                 SYSTEMS, DIGITAL - Distributed CONCEPTUAL KNOWLEDGE
                 REPRESENTATION DISTRIBUTED REPRESENTATIONS DISTRIBUTED
                 SEMANTIC REPRESENTATIONS NATURAL LANGUAGE PROCESSING
                 CONNECTIONIST ARCHITECTURES Abstract ONLY",
}

@InProceedings{cnlp:miikkulainen89,
  title =        "Modular neural network architecture for sequential
                 paraphrasing of script-based stories.",
  author =       "R. Miikkulainen and M. G. Dyer",
  year =         "1989",
  pages =        "49--56",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  booktitle =    "IJCNN International Joint Conference on Neural
                 Networks, Washington, DC, June 1989",
  abstract =     "Sequential recurrent neural networks have been applied
                 to a fairly high-level cognitive task, i.e.,
                 paraphasing script-based stories. Using hierarchically
                 organized modular subnetworks, which are trained
                 separately and in parallel, the complexity of the task
                 is reduced by effectively dividing it into subgoals.
                 The system uses sequential natural language input and
                 output and develops its own I/O representations for the
                 words. The representations are stored in an external
                 global lexicon and are adjusted in the course of
                 training by all four subnetworks simultaneously,
                 according to the FGREP-method. By concatenating a
                 unique identification with the resulting
                 representation, an arbitrary number of instances of the
                 same word type can be created and used in the stories.
                 The system is able to produce a fully expanded
                 paraphrase of the story from only a few sentences,
                 i.e., the unmentioned events are inferred. The word
                 instances are correctly bound to their roles, and
                 simple plausible inferences of the variable content of
                 the story are made in the process. 14 Refs.",
  keywords =     "SYSTEMS SCIENCE AND CYBERNETICS - Cognitive Systems
                 DATA PROCESSING MODULAR NEURAL NETWORK SEQUENTIAL
                 RECURRENT NEURAL NETWORKS SEQUENTIAL PARAPHRASING
                 NATURAL LANGUAGE PROCESSING FGREP-METHOD HIGH LEVEL
                 COGNITIVE TASK",
}

@InProceedings{cnlp:weber,
  title =        "A Connectionist model of conceptual representation.",
  author =       "S. H. Weber",
  year =         "1989",
  pages =        "477--483",
  publisher =    "IEEE, IEEE Service Center, Piscataway, NJ, USA.
                 Available from IEEE Service Cent (cat n 89CH2765-6),
                 Piscataway, NJ, USA.",
  address =      "Dep of Comput Sci, Univ of Rochester, Rochester, New
                 York",
  booktitle =    "IJCNN International Joint Conference on Neural
                 Networks Washington, DC",
  abstract =     "A description is given of a connectionist architecture
                 for modeling conceptual representation. Connectionist
                 structures for capturing property-value bindings,
                 conceptual aspects, scalar properties, property
                 abstraction and inheritance, and category error
                 detection are described. With these structures, the
                 system known as DIFICIL (Direct Inferences and
                 Figurative Interpretation in a Connectionist
                 Implementation of Language comprehension) is able to
                 perform direct inferences, both immediate and mediated,
                 and interpret both literal and figurative
                 adjective-noun combinations. 9 Refs.",
  keywords =     "SYSTEMS SCIENCE AND CYBERNETICS - Neural Nets COMPUTER
                 ARCHITECTURE CONNECTIONIST MODEL CONCEPTUAL
                 REPRESENTATION DIFICIL SYSTEM LANGUAGE COMPREHENSION",
}

@InProceedings{cnlp:cole,
  title =        "Language identification with neural networks: {A}
                 feasibility study.",
  author =       "R. A. Cole and J. W. T. Inouye and Y. K. Muthusamy and
                 M. Gopalakrishnan",
  year =         "1989",
  pages =        "525--529",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  booktitle =    "IEEE Pacific RIM Conference on Communications,
                 Computers and Signal Processing, Victoria, Canada, June
                 1989",
  abstract =     "The feasibility of an approach to automatic language
                 identification that combines recent advances in
                 computer speech recognition and artificial neural
                 networks is discussed. It is shown that artificial
                 neural networks can be used as pattern classifiers that
                 use information about distributions of broad phonetic
                 categories to identify languages. Using artificial
                 languages that differ only by their distribution of
                 stop consonants, feature vectors were extracted from
                 varying amounts of speech from each language. These
                 feature vectors were then used to train an artificial
                 neural network using the back-propagation algorithm.
                 Classification results for two different sets of
                 artificial languages are presented. 14 Refs.",
  keywords =     "speech - recognition systems science and cybernetics -
                 neural nets database systems computer speech
                 recognition artificial neural networks stop consonants
                 artificial languages back-propagation algorithm drive
                 reinforcement neuron",
}

@InProceedings{cnlp:barnden88,
  title =        "Simulations of Conposit, a supra-connectionist
                 architecture for commonsense reasoning.",
  author =       "J. A. Barnden",
  year =         "1988",
  pages =        "311--315",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  booktitle =    "Proceedings: The 2nd Symposium on the Frontiers of
                 Massively Parallel Computations, Fairfax, Virginia,
                 October 1988",
  abstract =     "A computation architecture called 'Conposit' is
                 outlined. Composit manipulates very-short-term complex
                 symbolic data structures of types that are useful in
                 high-level cognitive tasks such as commonsense
                 reasoning, planning, and natural language
                 understanding. Conposit's data structures are,
                 essentially, temporary configurations of symbol
                 occurrences in a two-dimensional array of registers.
                 Each register is implementable as a neural subnetwork
                 whose activation pattern realizes the symbol
                 occurrence. The data structures are manipulated by
                 condition-action rules that are realizable as further
                 neural subnetworks attached to the array. In
                 simulations, Conposit performs symbolic processing of
                 types previously found difficult for
                 connectionist/neural networks. A version of Conposit,
                 simulated on the massively parallel processor,
                 embodying core aspects of P. Johnson-Laird's mental
                 model theory (1983) of human syllogistic reasoning is
                 concentrated on. This version illustrates Conposit's
                 power and flexibility, which arises from two unusual
                 data-structure encoding techniques: relative-position
                 encoding and pattern-similarity association. 12 Refs.",
  keywords =     "COMPUTER SYSTEMS, DIGITAL - Parallel Processing
                 COMPUTER ARCHITECTURE SYSTEMS SCIENCE AND CYBERNETICS -
                 Cognitive Systems ARTIFICIAL INTELLIGENCE -
                 Applications COGNITIVE MODELING COMMONSENSE REASONING
                 KNOWLEDGE REPRESENTATION NEURAL NETWORKS",
}

@Article{cnlp:wheately,
  title =        "Connectionist model of children's comprehension and
                 production of simple English sentences.",
  author =       "B. Wheatley",
  journal =      "Neural Networks",
  year =         "1988",
  volume =       "1",
  number =       "1 SUPPL",
  pages =        "321",
  address =      "Univ of Wisconsin-Milwaukee, Milwaukee, Wisconsin",
  booktitle =    "International Neural Network Society 1988 First Annual
                 Meeting Boston, Massachussetts",
  abstract =     "Parallel distributed processing (connectionist)
                 systems provide a promising model for complex cognitive
                 systems like natural language. By constructing a
                 connectionist network and observing its behavior, it is
                 possible to model and test new hypotheses about
                 long-standing questions in natural language research.
                 Studies of young children have found that production of
                 simple English sentences may be superior to
                 comprehension in the absence of nonlinguistic cues. A
                 Boltzmann machine network was constructed, trained, and
                 tested for comprehension and production. The network
                 consists of two overlapping subnetworks, one
                 representing pragmatic knowledge and one representing
                 syntactic knowledge. The set of units representing the
                 meaning of a sentence is shared between the two
                 subnetworks. 2 Refs.",
  keywords =     "SYSTEMS SCIENCE AND CYBERNETICS - Cognitive Systems
                 AUTOMATA THEORY SPEECH SUMMARY ONLY CONNECTIONIST MODEL
                 CHILDREN'S COMPREHENSION ENGLISH SENTENCES BOLTZMANN
                 MACHINE NETWORK SYNTACTIC KNOWLEDGE",
}

@Article{cnlp:charny,
  title =        "Noun compound understanding using neural networks.",
  author =       "M. Charny",
  journal =      "Neural Networks",
  year =         "1988",
  volume =       "1",
  number =       "1 SUPPL",
  pages =        "293",
  address =      "MITRE Corp, McLean, Virginia",
  booktitle =    "International Neural Network Society 1988 First Annual
                 Meeting Boston, Massachussetts",
  abstract =     "Noun compounds are groups of two or more nouns which
                 bring together separate concepts to form a new or
                 altered concept, e.g., tea rose garden owner. Their
                 meaning cannot be discovered by syntactic parsing but
                 must employ semantic processing of some type.
                 Understanding these compounds is a task humans
                 generally do well but machines do poorly by contrast.
                 They are an important part of natural language
                 understanding and are a good testbed for the
                 development of cognitive systems. Our current work
                 employs a neural network paradigm to approach the
                 problem of 'understanding' these noun compounds. We use
                 a fully connected multilayer perceptron system with
                 backward error propagation learning rule. 3 Refs.",
  keywords =     "SPEECH - Recognition SYSTEMS SCIENCE AND CYBERNETICS -
                 Cognitive Systems AUTOMATA THEORY SUMMARY ONLY NOUN
                 COMPOUNDS SEMANTIC PROCESSING NATURAL LANGUAGE
                 UNDERSTANDING MULTILAYER PERCEPTRON NEURAL NETWORKS",
}

@Article{cnlp:wang88,
  title =        "Three neural models which process temporal
                 information.",
  author =       "D. Wang and I. K. King",
  journal =      "Neural Networks",
  year =         "1988",
  volume =       "1",
  number =       "1 SUPPL",
  pages =        "227",
  address =      "USC Dep of Computer Science, Los Angeles, California",
  booktitle =    "International Neural Network Society 1988 First Annual
                 Meeting Boston, Massachussetts",
  abstract =     "Using temporal summation mechanism of a single synapse
                 which is modeled and implemented in SLONN simulation
                 language (Wang \& Hsu, 1988), we propose three neural
                 models that can deal with three different aspects of
                 temporal information processing. The first model will
                 separate multiple temporal patterns into an array of
                 neurons. Each neuron N//i in the array passes the
                 temporal patterns whose frequencies are greater than
                 and equal to certain value f//i. With one-sided lateral
                 inhibition mechanism in the second model, each neuron
                 N//i will only pass the temporal patterns whose
                 frequencies are closest to f//i. Therefore, N//i
                 behaves exactly as a frequency filter. By combining the
                 second model with short-term memory mechanism
                 (Grossberg, 1976) and associative learning, the third
                 model can be used to store and recall sequences of
                 temporal patterns. We describe them in some detail.
                 (Edited author abstract) 2 Refs.",
  keywords =     "SYSTEMS SCIENCE AND CYBERNETICS - Neural Nets
                 INFORMATION THEORY COMPUTER SIMULATION LANGUAGES
                 SUMMARY ONLY TEMPORAL INFORMATION SYNAPSE MULTIPLE
                 TEMPORAL PATTERNS FREQUENCY FILTER TEMPORAL SUMMATION",
}

@InProceedings{cnlp:pham,
  title =        "Mosaic: {A} macro-connectionist organization system
                 for artificial intelligence computation.",
  author =       "K. M. Pham and P. Degoulet",
  year =         "1988",
  pages =        "533--540",
  publisher =    "IEEE, New York, NY, USA. Available from IEEE Service
                 Cent (cat n 88CH2632-8) Piscataway, NJ, USA.",
  address =      "Hopital Broussais, Paris, France",
  booktitle =    "IEEE International Conference on Neural Networks San
                 Diego, California",
  abstract =     "The authors present MOSAIC, an expert-system generator
                 which uses amacroconnectionist approach to implement a
                 dynamic system of knowledge representation. Each
                 fragment of knowledge, termed a neuronic, integrates
                 both declarative and procedural knowledge
                 representation. The inference mechanism is fully
                 distributed over the knowledge base, which eliminates
                 the separation between knowledge and the treatment
                 process. The macroconnectionist approach is derived
                 from neurobiological models in which the basic unit
                 corresponds to a system of neurons and not to a single
                 neuron. This organization is positioned at an
                 intermediate level between cognitive psychology and
                 elementary neurobiology. However, the definition of a
                 neuronic is recursive and makes it possible to
                 integrate into the same system entities situated at
                 different levels of complexity. MOSAIC uses both
                 artificial-intelligence concepts (e.g., explicit
                 inference strategies) and connectionist concepts (e.g.,
                 inference mechanisms based on the propagation of
                 activation of dynamic contexts). A prototype based on
                 these concepts has been constructed and its first
                 medical application is being tested. 22 Refs.",
  keywords =     "ARTIFICIAL INTELLIGENCE - Expert Systems SYSTEMS
                 SCIENCE AND CYBERNETICS - Cognitive Systems MOSAIC
                 SYSTEM MACROCONNECTIONIST APPROACH KNOWLEDGE
                 REPRESENTATION NEUROBIOLOGICAL MODELS COGNITIVE
                 PSYCHOLOGY",
}

@InProceedings{cnlp:nolfi,
  title =        "Learning to understand sentences in a connectionist
                 network.",
  author =       "S. Nolfi and ?. Parisi",
  year =         "1988",
  pages =        "215--219",
  publisher =    "IEEE, New York, NY, USA. Available from IEEE Service
                 Cent (cat n 88CH2632-8) Piscataway, NJ, USA.",
  address =      "Fondazione Sigma Tau, Rome, Italy",
  booktitle =    "IEEE International Conference on Neural Networks San
                 Diego, California",
  abstract =     "Language understanding implies understanding of
                 subject, verb, and object, or who did what to whom. The
                 authors describe this as establishing the correct
                 'assembly links' between pairs of words in the input
                 sentence. Some experiments are reported in which a
                 network with memory units learns to understand
                 sentences by incorporating during learning various
                 constrains which are activated by word order,
                 morphology, and prepositional marking of nouns. 5
                 Refs.",
  keywords =     "SPEECH - Intelligibility SYSTEMS SCIENCE AND
                 CYBERNETICS - Learning Systems DATA STORAGE UNITS
                 CONNECTIONIST NETWORK LANGUAGE UNDERSTANDING",
}

@InProceedings{cnlp:ricotti,
  title =        "Learning of word stress in a sub-optimal second order
                 back-propagation neural network.",
  author =       "L. P. Ricotti and S. Ragazzini and G. Martinelli",
  year =         "1988",
  pages =        "355--361",
  publisher =    "IEEE, New York, NY, USA. Available from IEEE Service
                 Cent (cat n 88CH2632-8) Piscataway, NJ, USA.",
  address =      "Fondazione Ugo Bordoni, Rome, Italy",
  booktitle =    "IEEE International Conference on Neural Networks San
                 Diego, California",
  abstract =     "The authors show an example of an efficient and easy
                 solution, using a neural network, of a problem that
                 cannot be easily solved with rules. This example
                 regards the localization of primary word stress. The
                 knowledge of the position of primary stress is very
                 useful in text-to-speech synthesis of Italian, a
                 language characterized by a very prominent word accent.
                 In fact, the position of word stress is the basis for
                 the automatic generation of the pattern of duration of
                 the syllables and of the intonation of the whole
                 phrase. The authors use a feedforward network with an
                 error back-propagation learning, extending the method
                 with the computation of the correction step based on
                 the second derivative of the error function. This
                 method has been used to speed up convergence without
                 using a fixed learning rate and a momentum term. The
                 authors obtain a steep decrease of the error at the
                 expense of a limited increase of the computational
                 cost. To effectively use the method, it is, however,
                 necessary to control some conditions that must be
                 satisfied to use the second derivative as an estimate
                 of the correction step. 3 Refs.",
  keywords =     "SYSTEMS SCIENCE AND CYBERNETICS - Learning Systems
                 SPEECH - Synthesis BACK-PROPAGATION NEURAL NETWORK
                 TEXT-TO-SPEECH SYNTHESIS LEARNING OF WORD STRESS XOR
                 PROBLEM ERROR BACK-PROPAGATION LEARNING",
}

@InProceedings{cnlp:lendaris,
  title =        "Conceptual graph knowledge systems as problem context
                 for neural networks.",
  author =       "G. G. Lendaris",
  year =         "1988",
  pages =        "133--140",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  booktitle =    "IEEE International Conference on Neural Networks, San
                 Diego, California, July 1988",
  abstract =     "For a connectionist network to be able to learn to
                 generalize well, there must be some correspondence
                 between the structure/constraints of the net's
                 architecture and those of the given problem space.
                 Therefore, recourse to experiments with real-world
                 problems will always be required in connectionist
                 research. The author gives a outline of a problem area
                 for which connectionist nets hold great promise:
                 knowledge systems, where the knowledge is
                 encoded/represented using conceptual graphs. Certain
                 aspects of this problem context are already known, and
                 these are probed for possible implementation by
                 connectionist nets. The approach used is to present
                 some basic properties of conceptual graphs, indicate
                 operations important in their application, and point
                 out those that might be candidates for implementation
                 with neural nets. A special representation schema for
                 conceptual graphs is used for their implementation by
                 neural nets. 8 Refs.",
  keywords =     "systems science and cybernetics - neural nets database
                 systems artificial intelligence conceptual graph
                 knowledge systems connectionist network problem
                 context",
}

@InProceedings{cnlp:smolensky87,
  title =        "Analysis of distributed representation of constituent
                 structure in connectionist systems.",
  author =       "P. Smolensky",
  year =         "1987",
  pages =        "47",
  publisher =    "IEEE, New York, NY, USA. Available from IEEE Service
                 Cent (Cat n 87CH2386-1), Piscataway, NJ, USA",
  address =      "Univ of Colorado, Boulder, Colorado",
  booktitle =    "1987 IEEE Conference on Neural Information Processing
                 Systems - Natural and Synthetic. Denver, Colorado",
  abstract =     "Summary form only given, as follows. A general
                 analysis of theconnectionist representation of complex
                 symbolic structures (e. g., strings, stacks, trees) is
                 reported. A general scheme is formulated called the
                 tensor product representation, which generalizes a
                 number of the representational approaches found in
                 existing connectionist systems. The tensor product
                 representation can be proved to possess a number of the
                 properties that are required of a computationally
                 adequate connectionist representation.",
  keywords =     "SYSTEMS SCIENCE AND CYBERNETICS - Neural Nets COMPUTER
                 ARCHITECTURE - Mathematical Models CONNECTIONIST
                 SYSTEMS CONSTITUENT STRUCTURE DISTRIBUTED
                 REPRESENTATION",
}

@InProceedings{cnlp:walters87a,
  title =        "{RESPONSE} {MAPPING} {FUNCTIONS}: {CLASSIFICATION}
                 {AND} {ANALYSIS} {OF} {CONNECTIONIST}
                 {REPRESENTATIONS}.",
  author =       "D Walters",
  year =         "1987",
  pages =        "III/79--86",
  publisher =    "SOS Printing, San Diego, CA, USA. Available from IEEE
                 Service Cent (cat n 87TH0191-7), Piscataway, NJ, USA",
  address =      "State Univ of New York, Buffalo, New York",
  booktitle =    "IEEE First International Conference on Neural
                 Networks. San Diego, California",
  abstract =     "A theoretical framework is presented in which
                 connectionist representations can be classified and
                 analyzed. The theoretical classification of
                 connectionist variable representations has three
                 purposes: to express existing variable representations
                 in unified terms; to enable representations to be
                 formally analyzed in a uniform manner; and to suggest
                 new variable representations. In the the course of
                 developing the theoretical framework for variable
                 representations, it is shown that the process of
                 measuring perceptual features or variables determines
                 the type of connectionist representation for those
                 features. This concept is further developed in a
                 response mapping function analysis of distributed
                 encodings.",
  keywords =     "MATHEMATICAL TECHNIQUES - Function Evaluation CODES,
                 SYMBOLIC - Encoding SYSTEMS SCIENCE AND CYBERNETICS -
                 Neural Nets MAPPING FUNCTIONS CONNECTIONIST
                 REPRESENTATIONS Variable REPRESENTATIONS DISTRIBUTED
                 ENCODINGS THEORETICAL CLASSIFICATION",
}

@InProceedings{cnlp:chun,
  title =        "A massively parallel model of schema selection.",
  author =       "H. W. Chun and A. Mimo",
  year =         "1987",
  pages =        "II/379--386",
  booktitle =    "IEEE First International Conference on Neural
                 Networks. San Diego, CA, June 1987",
  abstract =     "A massively parallel model of schema selection called
                 SAMPAN is presented. The SAMPAN system is a
                 constraint-satisfaction network with nodes that perform
                 simple pattern matching and input summation. It is
                 motivated by recent success in connectionist schema
                 representations and in natural language marker-passing
                 systems. A pure connectionist representation lacks
                 generality; new propositions cannot easily be
                 represented. SAMPAN gets around this problem by using
                 marker-passing to perform variable binding on
                 generalized concepts. This combination of
                 marker-passing with connectionist spreading activation
                 provides a highly malleable and general representation.
                 10 refs.",
  keywords =     "Systems Science and Cybernetics - Neural Nets Computer
                 Systems, Digital - Parallel Processing Artificial
                 Intelligence Massively Parallel Model Schema Selection
                 Sampan System Constraint-Satisfaction Network
                 Connectionist Networks",
}

@InProceedings{cnlp:geffner,
  title =        "On the probabilistic semantics of connectionist
                 networks.",
  author =       "H. Geffner and J. Pearl",
  year =         "1987",
  pages =        "II/187--195",
  publisher =    "SOS Printing",
  address =      "Available from IEEE,Piscataway, NJ",
  booktitle =    "IEEE First International Conference on Neural
                 Networks. San Diego, CA, June 9187",
  abstract =     "The goodness/energy paradigm has recently emerged as a
                 useful framework for the construction and analysis of
                 connectionist models. Its lack of a clear semantics,
                 however, has made it unsuitable as an specification
                 language for the declarative content of those models. A
                 correspondence is established between connectionist
                 networks and a well-known family of probabilistic
                 networks, thus endowing connectionist models with a
                 well-understood probabilistic semantics. It is shown
                 how a natural extension of the energy formulation
                 presented by J. J. Hopfield (1982) leads to models
                 capable of expressing arbitrary probability
                 distributions. 12 refs.",
  keywords =     "systems science and cybernetics - neural nets
                 artificial intelligence probabilistic semantics
                 connectionist networks goodness/energy paradigm
                 probabilistic networks",
}

@Article{cnlp:partridge,
  title =        "{CONNECTIONIST} {NETWORKS} {QUA} {GRAPHS}.",
  author =       "D. Partridge",
  journal =      "Computers \& Mathematics with Applications 86 V15 N4
                 1988, First N M Symp on Graph Theor Models in Comput
                 Sci, Las Cruces, NM, USA, Apr",
  year =         "1988",
  pages =        "325--331",
  ISBN =         "0097-4943",
  address =      "New Mexico State Univ, Las Cruces, New Mexico",
  abstract =     "Connections networks are used to represent knowledge
                 in terms of'subsymbolic' nodes. The connectionist
                 paradigm is seen as a promising new approach to the
                 realization of intelligent systems, and one that may be
                 particularly amenable to formal analysis. This paper
                 introduces connectionism, points out some of the major
                 problems and argues that a graph-theoretical approach
                 to some of the recognized problems may prove fruitful.
                 (Edited author abstract) 17 refs.",
  keywords =     "ARTIFICIAL INTELLIGENCE MATHEMATICAL TECHNIQUES -
                 Graph Theory KNOWLEDGE REPRESENTATION SEMANTIC NETWORKS
                 CONNECTIONIST NETWORKS",
}

@InProceedings{cnlp:gigley,
  title =        "{SENTENCE} {COMPREHENSION} {PROCESSING} - {A}
                 {SERIAL}-{ORDER}, {TIME}-{SYNCHRONOUS} {PROCESS}.",
  author =       "H. M. Gigley",
  journal =      "Digest of Papers - IEEE Computer Society International
                 Conference 32nd",
  year =         "1987",
  pages =        "39--42",
  publisher =    "IEEE, New York, NY, USA. Available from IEEE Service
                 Cent (Cat n 87CH2409-1), Piscataway, NJ, USA",
  ISBN =         "0-8186-0764-5",
  address =      "Univ of New Hampshire, Durham, New Hampshire",
  booktitle =    "Digest of Papers - COMPCON Spring 87: Thirty-Second
                 IEEE Computer Society International Conference. San
                 Francisco, California",
  abstract =     "A description is given of HOPE, a neuro-based
                 computational model ofsingle-sentence-comprehension
                 processing. An AI model, it focuses on syntactically
                 based disambiguation within a processing paradigm based
                 on the types of ambiguity found in neural processing.
                 It addresses performance issues reported in
                 psycholinguistic studies of normal performance as well
                 as neurolinguistic ones under lesion conditions. HOPE
                 includes a facility for studying processing
                 desynchronization as a possible cause of lesion
                 performance behavior as well as a facility for studying
                 the effects of knowledge dissolution on language
                 performance. 16 refs.",
  keywords =     "SPEECH - Recognition SYSTEMS SCIENCE AND CYBERNETICS
                 BIOMEDICAL ENGINEERING - Neurophysiology PATTERN
                 RECOGNITION ARTIFICIAL INTELLIGENCE - Expert Systems
                 NATURAL LANGUAGE PROCESSING (NLP) PSYCHOLINGUISTICS
                 NEUROLINGUISTICS HOPE MODEL SENTENCE COMPREHENSION",
}

@InProceedings{cnlp:wong,
  title =        "{TOWARD} {A} {MASSIVELY} {PARALLEL} {SYSTEM} {FOR}
                 {WORD} {RECOGNITION}.",
  author =       "M. K. Wong and H. W. Chun",
  year =         "1986",
  pages =        "1967--1970",
  publisher =    "IEEE, New York, NY, USA. Available from IEEE Service
                 Cent (Cat n 86 CH2243-4), Piscataway, NJ, USA",
  ISBN =         "0736-7791",
  address =      "GTE Lab Inc, Waltham, Massachussetts",
  booktitle =    "ICASSP 86 - Proceedings, IEEE-IECEJ-ASJ International
                 Conference on Acoustics, Speech, and Signal Processing.
                 Tokyo, Japan",
  abstract =     "The authors describe a massively parallel system for
                 word recognition.Based on the connectionist network
                 model adopted from cognitive science and artificial
                 intelligence, the system consists of a large number of
                 simple neuronlike processing units, or nodes, which
                 represent words, phonetic segments, or phonetic
                 features. The computation consists of constant updating
                 of activation levels of all nodes, resulting from the
                 excitatory links and inhibitory links between the
                 nodes. Input to the system consists of frame-by-frame
                 scores of similarity to a set of phonetic segments
                 necessary for distinguishing between words in the
                 vocabulary. These similarity scores are combined into
                 phonetic feature indexes for each frame of speech as
                 input to the feature nodes in the network. A linguistic
                 knowledge base is built into the nentwork, allowing
                 both data-driven processing and top-down prediction to
                 cooperate or compete in working toward the correct
                 lexical hypothesis. 12 refs.",
  keywords =     "COMPUTER SYSTEMS, DIGITAL - Parallel Processing SPEECH
                 - Recognition ARTIFICIAL INTELLIGENCE INFORMATION
                 SCIENCE - Language Translation and Linguistics SYSTEMS
                 SCIENCE AND CYBERNETICS - Neural Nets MASSIVELY
                 PARALLEL SYSTEM WORD RECOGNITION FRAME-BY-FRAME
                 SIMILARITY SCORES PHONETIC SEGMENTS PHONETIC FEATURE
                 INDEXES",
}

@InProceedings{cnlp:li,
  title =        "Massively parallel network-based natural language
                 parsing system.",
  author =       "T. Li and H. W. Chun",
  year =         "1987",
  pages =        "401--408",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  ISBN =         "0-8186-0780-7",
  booktitle =    "Second International Conference on Computers and
                 Applications. Beijing, China, June 1987",
  abstract =     "A massively parallel network-based parsing scheme
                 (MPNP) organizedaccording to context-free grammar rules
                 is described. The scheme combines the advantages of
                 connectionist networks with marker-passing algorithms.
                 The simple spreading-activation algorithm in
                 connectionist networks provide a basis for an
                 expectation propagation model. The marker-passing
                 algorithm permits this parsing scheme to produce all
                 alternatives in parallel and generates all plausible
                 parses as the output. The approach extends massively
                 parallel parsing models to handle recursive definitions
                 of grammar and sentences of arbitrary length. In
                 addition, it eliminates irrelevant deductions without
                 explicit heuristic knowledge by combining a bottom-up
                 parsing algorithm with a top-down expectation
                 propagation model. The approach has been used
                 successfully to parse both English and Chinese
                 sentences. 16 refs.",
  keywords =     "artificial intelligence computer programming -
                 algorithms computer systems, digital - parallel
                 processing information science - language translation
                 and linguistics natural language parsing system
                 spreading-activation algorithms connectionist networks
                 marker-passing algorithm expectation propagation
                 model",
}

@InProceedings{cnlp:walters87b,
  title =        "Representation Of Variables In Connectionist
                 Networks.",
  author =       "D. K. W. Walters",
  year =         "1987",
  pages =        "698--702",
  publisher =    "IEEE, New York, NY, USA. Available from IEEE Service
                 Cent (Cat n 87CH2465-3), Piscataway, NJ, USA",
  ISBN =         "0-8186-0777-X",
  address =      "Univ of Buffalo, New York",
  booktitle =    "Proceedings - First International Conference on
                 Computer Vision. London, UK",
  abstract =     "There are various types of fine-grain parallel
                 architectures which areproving useful for artificial
                 intelligence research, ranging from neural networks,
                 through connectionist networks, to the Connection
                 Machine. The style of computing that is possible with
                 all such connectionist networks is different from that
                 possible with uniprocessor systems, or with parallel
                 systems containing a small number of processors. Just
                 as a different style of computation is possible in
                 fine-grain parallel systems, the styles of
                 representation of variables that are natural for such
                 parallelism are different from the types of
                 representation natural for serial, or coarse-grain
                 parallel processing. However, there has been no general
                 theoretical analysis of connectionist representations.
                 A language is provided in which different parallel
                 representations can be accurately described and the
                 differences between representations clarifed. The
                 theoretical framework for variable representations is
                 also useful for suggesting new types of
                 representations. A theoretical framework is developed
                 which can aid in the formal analysis of fine-grain
                 parallel representations. 14 refs.",
  keywords =     "ARTIFICIAL INTELLIGENCE SYSTEMS SCIENCE AND
                 CYBERNETICS - Neural Nets IMAGE PROCESSING - Theory
                 CONNECTIONIST NETWORKS PARALLEL ARCHITECTURES
                 CONNECTION MACHINE",
}

@InProceedings{cnlp:shnelle,
  title =        "{CONCURRENT} {PARSING} {IN} Programmable {LOGIC}
                 {ARRAY} ({PLA}-) {NETS} - {PROBLEMS} {AND}
                 {PROPOSALS}.",
  author =       "H. Schnelle",
  year =         "1984",
  pages =        "150--153",
  publisher =    "Assoc for Computational Linguistics, Menlo Park, CA,
                 USA",
  address =      "Ruhr-Univ Bochum, Bochum, West Germany",
  booktitle =    "10th International Conference on Computational
                 Linguistics, 22nd Annual Meeting of the Association for
                 Computational Linguistics, Proceedings of Coling 84.
                 Palo Alto, California",
  abstract =     "The author provides a conceptual and practical
                 introduction into theprinciples of wiring or
                 constructing special machines for language processing
                 tasks instead of programming a universal machine.
                 Construction would, in principle, provide higher
                 descriptive adequacy in computationally based
                 linguistics. (Edited author abstract) 2 refs.",
  keywords =     "AUTOMATA THEORY - Computational Linguistics SYSTEMS
                 SCIENCE AND CYBERNETICS - Neural Nets SIGNAL PROCESSING
                 LOGIC CIRCUITS PROCESS-ORIENTED LINGUISTICS VLSI DESIGN
                 METHODS CELLULAR AUTOMATA OPERATIONAL PRIMITIVES TEST
                 PREDICTION Programmable LOGIC ARRAY (PLA)",
}

@InProceedings{cnlp:reilly84,
  title =        "{CONNECTIONIST} {MODEL} {OF} {SOME} {ASPECTS} {OF}
                 {ANAPHOR} {RESOLUTION}.",
  author =       "R. G. Reilly",
  year =         "1984",
  pages =        "144--149",
  publisher =    "Assoc for Computational Linguistics, Menlo Park, CA,
                 USA",
  address =      "St. Patrick's Coll, Dublin, Irel",
  booktitle =    "10th International Conference on Computional
                 Linguistics, 22nd Annual Meeting of the Association for
                 Computational Linguistics, Proceedings of Coling 84.
                 Palo Alto, California",
  abstract =     "The author describes some recent developments in
                 language processing involving computational models
                 which more closely resemble the brain in both structure
                 and function. These models employ a large number of
                 interconnected parallel computational units which
                 communicate via weighted levels of excitation and
                 inhibition. A specific model is described which uses
                 this approach to process some fragments of connected
                 discourse. (Edited author abstract) 7 refs.",
  keywords =     "AUTOMATA THEORY - Computational Linguistics SYSTEMS
                 SCIENCE AND CYBERNETICS - Brain Models CONNECTIONIST
                 MODELS ANAPHORA NETWORK PROGRAMMING LANGUAGE (NPL)
                 COGNITIVE MODELING WORD SENSE LEVEL SCHEMA LEVEL",
}

@InProceedings{cnlp:birnbaum,
  title =        "Lexical ambiguity as a touchstone for theories of
                 language analysis.",
  author =       "L. Birnbaum",
  year =         "1985",
  pages =        "815--820",
  ISBN =         "0-934613-02-8",
  booktitle =    "IJCAI 85: Proceedings of the Ninth International Joint
                 Conference on Artificial Intelligence. Los Angeles,
                 California, August 1985",
  abstract =     "This paper assesses several broad approaches to
                 language analysis with respect to the problem of
                 lexical ambiguity. The impact of the problem on both
                 syntactic and semantic analysis is discussed, and
                 several common methods for disambiguation, including
                 the use of selectional restrictions and scriptal
                 lexicons, are analyzed. Their shortcomings illustrate
                 the need for complex inference to resolve ambiguity,
                 which forms one of the key functional arguments in
                 favor of integrating language analysis with memory and
                 inference. However, it has proven surprisingly
                 difficult to realize such an integrated approach in
                 practice. The difficulty is shown to stem primarily
                 from the theories of memory and inferential processing
                 utilized. The implications for recent approaches to
                 language analysis based on connectionist mechanisms are
                 explored. Finally, the requirements imposed by lexical
                 disambiguation on theories of memory and inferential
                 processing are discussed. (Edited author abstract) 32
                 refs.",
  keywords =     "INFORMATION SCIENCE - Language Translation and
                 Linguistics LEXICAL AMBIGUITY NATURAL LANGUAGE ANALYSIS
                 DISAMBIGUATION MEMORY INFERENTIAL PROCESSING",
}

@InCollection{cnlp:cottrell,
  title =        "Viewing parsing as word sense discrimination: a
                 connectionist approach.",
  author =       "G. W. Cottrell and S. L. Small",
  booktitle =    "Computational Models of Natural Language Processing",
  year =         "1984",
  pages =        "91--119",
  publisher =    "North-Holland",
  address =      "Amsterdam",
  ISBN =         "0-444-87598-0",
  abstract =     "This paper advocates the interdisciplinary development
                 of a computationaltheory of human language
                 comprehension and proposes a collection of initial
                 constraints from which to start on such an enterprise.
                 In order to satisfy these constraints, our modeling
                 effort employs an architecture significantly different
                 from the typical computer and closer to that of the
                 human brain. We use a particular spreading activation
                 or active semantic network scheme, called
                 connectionism, which entails a massive number of
                 appropriately connected computing units that
                 communicate through weighted levels of excitation and
                 inhibition. While such an architecture does not solve
                 any problems per se, we believe that a number of
                 questions become easier to set forth and more
                 straightforward to solve. This paper surveys a number
                 of fundamental language comprehension issues from the
                 new perspective, and presents some simulation results
                 of a parsing model based on these considerations.
                 (Author abstract) 54 refs.",
  keywords =     "automata theory - computational linguistics systems
                 science and cybernetics - artificial intelligence human
                 language comprehension natural language processing
                 (nlp)",
}

@Article{cnlp:sharkey94b,
  title =        "Opening the black-box of connectionist nets - Some
                 lessons from Cognitive Science",
  author =       "N. E. Sharkey and A. J. C. Sharkey and S. A. Jackson",
  address =      "Univ Sheffield,Dept Comp Sci,Sheffield S10 2TN,S
                 Yorkshire,England",
  journal =      "Computer Standards \& Interfaces",
  year =         "1994",
  volume =       "16",
  number =       "3",
  pages =        "279--293",
  abstract =     "Connectionist networks are not simply opaque black
                 boxes with useful and efficient computational
                 properties. Rather they have important internal
                 structure that must be harnessed if their full
                 potential as a novel computing technology is to be
                 realised. First, the importance and usefulness of
                 opening the black box is discussed and research is
                 reviewed on how internal representations have been
                 studied and used in the Cognitive Science literature.
                 In the second section, a simple method for the
                 geometrical analysis of decision space is presented.
                 This shows connectionist networks as transparent boxes
                 in which their computational properties are clear. The
                 paper finishes with an example of how decision space
                 diagrams can be useful for investigating under what
                 circumstances it is best to adapt old weights for use
                 in novel tasks",
  keywords =     "CONNECTIONIST REPRESENTATION, BLACK BOX COMPUTING,
                 DECISION SPACE ANALYSIS, NEURAL NETWORKS, CLUSTER
                 ANALYSIS REPRESENTATION",
}

@Article{cnlp:zeng,
  title =        "Discrete Recurrent Neural Networks For Grammatical
                 Inference",
  author =       "Z. Zeng and R. M. Goodman and P. Smyth",
  address =      "Caltech,Dept Elect Engn,Pasadena,CA,91125 JET PROP
                 Lab,PASADENA,CA,91109",
  journal =      "IEEE Transactions on Neural Networks",
  year =         "1994",
  volume =       "5",
  number =       "2",
  pages =        "320--330",
  abstract =     "We describe a novel neural architecture for learning
                 deterministic context-free grammars, or equivalently,
                 deterministic pushdown automata. The unique feature of
                 the proposed network is that it forms stable state
                 representations during learning-previous work has shown
                 that conventional analog recurrent networks can be
                 inherently unstable in that they cannot retain their
                 state memory for long input strings. We have recently
                 introduced the discrete recurrent network architecture
                 for learning finite-state automata. Here we extend this
                 model to include a discrete external stack with
                 discrete symbols. A composite error function is
                 described to handle the different situations
                 encountered in learning. The pseudo-gradient learning
                 method (introduced in previous work) is in turn
                 extended for the minimization of these error functions.
                 Empirical trials validating the effectiveness of the
                 pseudo-gradient learning method are presented, for
                 networks both with and without an external stack.
                 Experimental results show that the new networks are
                 successful in learning some simple pushdown automata,
                 though overfitting and non-convergent learning can also
                 occur. Once learned, the internal representation of the
                 network is provably stable; i.e., it classifies unseen
                 strings of arbitrary length with 100% accuracy.",
  keywords =     "NEURAL NETWORKS, ROBOT PATH PLANNING, MULTIVARIATE
                 FUNCTION APPROXIMATION",
}

@Article{cnlp:gusgen,
  title =        "Connectionist Inference Systems",
  author =       "H. W. Gusgen and S. Holldobler",
  address =      "Gesell Math \& Datenverarbeitung Gmbh,Schloss
                 Birlinghoven,W-5205 St Augustin 1,Germany Th
                 Darmstadt,Fb Informat,Fg Intellekt,W-6100
                 Darmstadt,Germany",
  journal =      "Lecture Notes In Artificial Intelligence",
  year =         "1992",
  volume =       "590",
  pages =        "82--120",
  abstract =     "This paper presents a survey of connectionist
                 inference systems.",
  keywords =     "NEURAL NETWORKS, REPRESENTATION, OPTIMIZATION,
                 RELAXATION, ALGORITHM, HOPFIELD",
}

@Article{cnlp:wallace,
  title =        "Knowledge Representation And Decision-Making - {A}
                 Hybrid Approach",
  author =       "I. Wallace and S. Goss and K. Bluff",
  address =      "Swinburne Inst Technol,Pob 218,Hawthorn,Vic
                 3122,Australia Aeronaut Res Lab,Fishermans Bend,Vic
                 3207,Australia",
  journal =      "Lecture Notes In Artificial Intelligence",
  year =         "1992",
  volume =       "604",
  pages =        "525--528",
  abstract =     "Knowledge representation is a fundamental issue in the
                 simulation ofhuman performance such as the behaviour of
                 a pilot engaged in aircombat. Interaction between
                 symbolic and subsymbolic networks is used in a
                 cognitive architecture to represent the effects of
                 emotional and motivational processes on decision
                 making. This enables a shift from the conventional
                 focus of combat simulation on optimal decision making
                 to the modelling of authentic human decision making
                 with its individual variability and imperfection.",
  keywords =     "CONTEXT-DEPENDENT SPELLING CORRECTION, GRAMMAR
                 CHECKING, NATURAL-LANGUAGE-PROCESSING MODELS, NEURAL
                 NET CLASSIFIERS, N-GRAM ANALYSIS, OPTICAL CHARACTER
                 RECOGNITION (OCR), SPELL CHECKING, SPELLING ERROR
                 DETECTION, SPELLING ERROR PATTERNS,
                 STATISTICAL-LANGUAGE MODELS, WORD RECOGNITION AND
                 CORRECTION",
}

@Article{cnlp:machado,
  title =        "The Combinatorial Neural Network - {A} Connectionist
                 Model For Knowledge Based Systems",
  author =       "R. J. Machado and A. F. Darocha",
  address =      "Ibm Brazil,Ctr Rio Sci,Estr Canoa 3520,Br-22610 Rio
                 Janeiro,Brazil Unicamp,Inst Biol,Br-13100
                 Campinas,Brazil",
  journal =      "Lecture Notes In Computer Science",
  year =         "1991",
  volume =       "521",
  pages =        "578--587",
  abstract =     "This paper describes the Combinatorial Neural Model, a
                 high order neural network suitable for classification
                 tasks. The model is based on the fuzzy sets theory,
                 neural sciences and expert knowledge analysis results.
                 The model presents Interesting properties such as:
                 modularity, explanation capacity, concomitant knowledge
                 and data representation, high speed of training,
                 incremental learning, generalization capacity, feature
                 selection, processing of uncertain and Incomplete data,
                 fault tolerance.",
  keywords =     "NEURAL NETWORKS, LEARNING, CONNECTIONIST EXPERT
                 SYSTEMS",
}

@Article{cnlp:narazaki,
  title =        "A Connectionist Approach For Rule-Based Inference
                 Using An Improved Relaxation Method",
  author =       "H. Narazaki and A. L. Ralescu",
  address =      "Tokyo Inst Technol,Dept Syst Sci,4259 Nagatsuta,Midori
                 Ku,Yokohama,Kanagawa 227,Japan Lab Int Fuzzy Engn,Naka
                 Ku,Yokohama 231,Japan",
  journal =      "Ieee Transactions On Neural Networks",
  year =         "1992",
  volume =       "3",
  number =       "5",
  pages =        "741--751",
  abstract =     "This paper describes a connectionist mechanism for an
                 inference problem alternative to the usual chaining
                 method. Our inference problem is within the scope of
                 propositional logic that contains no variables and with
                 some enhanced knowledge representation facilities.
                 Basically, our method is an application of mathematical
                 programming where knowledge and data are transformed
                 into constraint equations. In our network, the nodes
                 represent propositions and constraint equations, and
                 the violation of constraints is formulated as an energy
                 function The inference is realized as a minimization
                 process of the energy function using the relaxation
                 method to search for a truth value distribution that
                 achieves the optimum consistency with the given
                 knowledge and data. We propose a modified relaxation
                 method to improve the computational inefficiencies
                 associated with the optimization process. In contrast
                 to the conventional microscopic inference technique
                 based on local and piecewise evaluations of the
                 knowledge, our method is macroscopic in that the whole
                 knowledge is taken into consideration simultaneously.
                 This aspect is vital to the inference under uncertainty
                 or conflicting knowledge. We demonstrate and analyze
                 the behavior of our method through examples of
                 deductive and abductive inference and of inference with
                 unorganized knowledge.",
}

@Article{cnlp:wyard,
  title =        "Grammar Recognition By a Single Layer Higher-Order
                 Neural Net",
  author =       "P. J. Wyard and C. Nightingale",
  address =      "BT Labs,Martlesham Heath,Ipswich 1P5
                 7RE,Suffolk,England",
  journal =      "BT Technology Journal",
  year =         "1992",
  volume =       "10",
  number =       "3",
  pages =        "77--96",
  abstract =     "A single layer higher order dynamic topology neural
                 net called HODYNE is described, targeted at natural
                 language processing problems. HODYNE has an input layer
                 of nodes containing concatenations of words from the
                 input string, and grows nodes during training which are
                 dependent on the training data. The training procedure
                 also has some novel features.Results are presented on
                 the problem of grammaticality determination. The
                 results are encouraging, with almost perfect
                 performance on unseen test sets derived from simple
                 context-free grammars, and promising performance on
                 sentences taken from the LOB Corpus of British English.
                 On the latter task, the performance of HODYNE is
                 compared with that of a method based on the statistics
                 of tag-pairs and with a multilayer perceptron.",
}

@Article{cnlp:hummel,
  title =        "Dynamic Binding in a Neural Network For
                 Shape-Recognition",
  author =       "J. E. Hummel and I. Biederman",
  journal =      "Psychological Review",
  year =         "1992",
  volume =       "99",
  number =       "3",
  pages =        "480--517",
  abstract =     "Given a single view of an object, humans can readily
                 recognize that object from other views that preserve
                 the parts in the original view. Empirical evidence
                 suggests that this capacity reflects the activation of
                 a viewpoint-invariant structural description specifying
                 the object's parts and the relations among them. This
                 article presents a neural network that generates such a
                 description. Structural description is made possible
                 through a solution to the dynamic binding problem:
                 Temporary conjunctions of attributes (parts and
                 relations) are represented by synchronized oscillatory
                 activity among independent units representing those
                 attributes. Specifically, the model uses synchrony (a)
                 to parse images into their constituent parts, (b) to
                 bind together the attributes of a part, and (c) to bind
                 the relations to the parts to which they apply. Because
                 it conjoins independent units temporarily, dynamic
                 binding allows tremendous economy of representation and
                 permits the representation to reflect the attribute
                 structure of the shapes represented.",
}

@Article{cnlp:giles,
  title =        "Learning and Extracting Finite State Automata with
                 2nd-Order Recurrent Neural Networks",
  author =       "C. L. Giles and C. B. Miller and D. Chen and H. H.
                 Chen and G. Z. Sun and Y. C. Lee",
  address =      "NEC RES INST,4 Independence Way,Princeton,NJ,08540
                 Univ Maryland,Inst Adv Comp Studies,Dept Phys \&
                 Astron,College PK,MD,20742",
  journal =      "Neural Computation",
  year =         "1992",
  volume =       "4",
  number =       "3",
  pages =        "393--405",
  abstract =     "We show that a recurrent, second-order neural network
                 using areal-time, forward training algorithm readily
                 learns to infer small regular grammars from positive
                 and negative string training samples. We present
                 simulations that show the effect of initial conditions,
                 training set size and order, and neural network
                 architecture. All simulations were performed with
                 random initial weight strengths and usually converge
                 after approximately a hundred epochs of training. We
                 discuss a quantization algorithm for dynamically
                 extracting finite state automata during and after
                 training. For a well-trained neural net, the extracted
                 automata constitute an equivalence class of state
                 machines that are reducible to the minimal machine of
                 the inferred grammar. We then show through simulations
                 that many of the neural net state machines are
                 dynamically stable, that is, they correctly classify
                 many long unseen strings. In addition, some of these
                 extracted automata actually outperform the trained
                 neural network for classification of unseen strings.",
}

@Article{cnlp:reilly92,
  title =        "A Connectionist Technique For Online Parsing",
  author =       "R. G. Reilly",
  address =      "St Patricks Coll,Educ Res Ctr,Dublin 9,Ireland",
  journal =      "Network-Computation in Neural Systems",
  year =         "1992",
  volume =       "3",
  number =       "1",
  pages =        "37--45",
  abstract =     "A technique is described that permits the on-line
                 construction and dynamic modification of parse trees
                 during the processing of sentence-like input. The
                 approach is a combination of simple recurrent network
                 (SRN) and recursive auto-associative memory (RAAM). The
                 parsing technique involves teaching the SRN to build
                 RAAM representations as it processes its input item by
                 item. The approach is a potential component of a larger
                 connectionist natural language processing system, and
                 could also be used as a tool in the cognitive modelling
                 of language understanding. Unfortunately, the modified
                 SRN demonstrates a limited capacity for
                 generalization.",
  keywords =     "NEURAL NETWORKS, INTERNAL REPRESENTATION, STATE SPACE,
                 ORTHOGONAL COMPLEMENT METHOD, FEEDFORWARD NETWORK
                 SYNTHESIS, THRESHOLD LOGIC",
}

@Article{cnlp:sowa,
  title =        "Conceptual Graphs As {A} Universal Knowledge
                 Representation",
  author =       "J. F. Sowa",
  address =      "IBM CORP,SYST RES,500 COLUMBUS
                 AVE,THORNWOOD,NY,10594",
  journal =      "COMPUTERS \& MATHEMATICS WITH APPLICATIONS",
  year =         "1992",
  volume =       "23",
  number =       "2--5",
  pages =        "75--93",
  abstract =     "Conceptual graphs are a knowledge representation
                 language designed as a synthesis of several different
                 traditions. First are the semantic networks, which have
                 been used in machine translation and computational
                 linguistics for over thirty years, Second are the
                 logic-based techniques of unification, lambda calculus,
                 and Peirce's existential graphs. Third is the
                 linguistic research based on Tesniere's dependency
                 graphs and various forms of case grammar and thematic
                 relations. Fourth are the dataflow diagrams and Petri
                 nets, which provide a computational mechanism for
                 relating conceptual graphs to external procedures and
                 databases. The result is a highly expressive system of
                 logic with a direct mapping to and from natural
                 languages. The lambda calculus supports the definitions
                 for a taxonomic system and provides a general mechanism
                 for restructuring knowledge bases. With the
                 definitional mechanisms, conceptual graphs can be used
                 as an intermediate stage between natural languages and
                 the rules and frames of expert systems-an important
                 feature for knowledge acquisition and for help and
                 explanations. During the past five years, conceptual
                 graphs have been applied to almost every aspect of AI,
                 ranging from expert systems and natural language to
                 computer vision and neural networks. This paper surveys
                 conceptual graphs, their development from each of these
                 traditions, and the applications based on them.",
}

@Article{cnlp:shastri92,
  title =        "Structured Connectionist Models of Semantic Networks",
  author =       "L. Shastri",
  address =      "Univ Penn,Dept Comp \& Informat
                 Sci,Philadelphia,PA,19104",
  journal =      "Computers \& Mathematics With Applications",
  year =         "1992",
  volume =       "23",
  number =       "2--5",
  pages =        "293--328",
  abstract =     "In this paper, we review some connectionist
                 realizations of semantic networks. We focus on models
                 that are generally referred to as structured
                 connectionist models. ln addition to reviewing three
                 specific models, we discuss the intimate relationship
                 between semantic networks and connectionist models of
                 knowledge representation. We point out the need for
                 massively parallel realizations of semantic networks
                 and show that structured connectionism offers an
                 appropriate computational framework for doing so.",
  keywords =     "Semantic Networks, Expert System, Knowledge Processing
                 Neural Networks, Fast Learning Algorithms, Impact of
                 Connectionist Systems",
}

@Article{cnlp:pinker,
  title =        "Rules of Language",
  author =       "S. Pinker",
  address =      "MIT,Dept Brain \& Cognit Sci,Cambridge,MA,02139",
  journal =      "Science",
  year =         "1991",
  volume =       "253",
  number =       "5019",
  pages =        "530--535",
  abstract =     "Language and cognition have been explained as the
                 products of a homogeneous associative memory structure
                 or alternatively, of a set of genetically determined
                 computational modules in which rules manipulate
                 symbolic representations. Intensive study of one
                 phenomenon of English grammar and how it is processed
                 and acquired suggests that both theories are partly
                 right. Regular verbs (walk-walked) are computed by a
                 suffixation rule in a neural system for grammatical
                 processing; irregular verbs (run-ran) are retrieved
                 from an associative memory.",
}

@Article{cnlp:hanson90,
  title =        "What Connectionist Models Learn - Learning and
                 Representation In Connectionist Networks",
  author =       "S. J. Hanson and D. J. Burr",
  address =      "Siemens Res CTR,Learning \& Knowledge Acquisit
                 Grp,Princeton,NJ,08540 Bellcore,Artificial Intelligence
                 \& Commun Res GRP,Morristown,NJ,07960",
  journal =      "Behavioral and Brain Sciences",
  year =         "1990",
  volume =       "13",
  number =       "3",
  pages =        "471--488",
}

@Article{cnlp:hendler,
  title =        "But What is the Substance of Connectionist
                 Representation - Comments",
  author =       "J. Hendler",
  address =      "Univ Maryland,Dept Comp Sci,College PK,MD,20742",
  journal =      "Behavioral and Brain Sciences",
  year =         "1990",
  volume =       "13",
  number =       "3",
  pages =        "496--496",
}

@Article{cnlp:pinker88,
  author =       "S. Pinker and A. Prince",
  year =         "1988",
  title =        "On language and connectionism: Analysis of a parallel
                 distributed processing model of language acquisition.
                 Special Issue: Connectionism and symbol systems",
  journal =      "Cognition",
  volume =       "28",
  number =       "1--2",
  pages =        "73--193.",
}

@Article{cnlp:chappellHumphreys94,
  key =          "Chappell \& Humphreys",
  author =       "Mark Chappell and Michael S. Humphreys",
  title =        "An Auto-Associative Neural Network for Sparse
                 Representations: Analysis and Application to Models of
                 Recognition and Cued Recall",
  journal =      "Psychological Review",
  pages =        "103--128",
  volume =       "101",
  number =       "1",
  year =         "1994",
  location =     "CMU Hunt Library",
}

@Article{cnlp:cherkasskyLari-Najafi92,
  key =          "Cherkassky \& Lari-Najafi",
  author =       "Vladimir Cherkassky and Hossein Lari-Najafi",
  title =        "Data Representation for Diagnostic Neural Networks",
  journal =      "IEEE Expert",
  pages =        "43--53",
  volume =       "7",
  number =       "5",
  year =         "1992",
  location =     "CMU Engineering \& Science Library",
}

@Article{cnlp:anderson90,
  key =          "Anderson",
  author =       "James A. Anderson",
  title =        "Data Representation in Neural Networks",
  journal =      "AI Expert",
  pages =        "30--37",
  volume =       "5",
  number =       "6",
  year =         "1990",
  location =     "CMU E\&S Library",
}

@PhdThesis{cnlp:leung89,
  author =       "H. C. Leung",
  school =       "Massachusetts Institute of Technology",
  title =        "The Use of Artificial Neural Networks for Phonetic
                 Recognition",
  year =         "1989",
  annote =       "He used back prop nets to classify English phonemes
                 cut from continuous speech. The phonemes are from
                 Timit. The signal representation is an auditory model
                 of Seneff consisting of synchrony envelopes and mean
                 rate response. The representation is adjusted for
                 pitch; the coefficients are normalized so that their
                 sum, per frame, is 0. Input was averaged spectrum from
                 vowel thirds, plus context specifying the phoneme on
                 the left and right. Performance was 60 percent.",
}

@MastersThesis{cnlp:lucke89,
  author =       "H. Lucke",
  address =      "Trumpington Street, Cambridge CB2 1PZ, UK",
  school =       "Cambridge University Engineering Department",
  title =        "Compositional Representation: {A} representation of
                 linguistic data for neural network recognizers",
  year =         "1989",
}

@InProceedings{cnlp:luckeFallside90,
  author =       "H. Lucke and F. Fallside",
  address =      "Kobe, Japan",
  booktitle =    "Proceedings of the International Conference on Spoken
                 Language Processing",
  pages =        "1377--1380",
  title =        "Application of the Compositional Representation to to
                 Lexical Access using Neural Networks",
  year =         "1990",
}

@InProceedings{cnlp:robinson92-esca-tutorial,
  author =       "T. Robinson",
  booktitle =    "Proceedings of the {ESCA} workshop: Comparing Speech
                 Signal Representations",
  title =        "Artificial Neural Networks: {T}he Mole-Grips of the
                 Speech Scientist",
  year =         "1992",
}

@PhdThesis{cnlp:henseler93,
  author =       "Johan Henseler",
  title =        "Connections, Neurons and Activation, The Organization
                 of Representation in Artificial Neural Networks",
  school =       "University of Limburg",
  year =         "1993",
  address =      "Maastricht, Netherlands",
}

@InProceedings{cnlp:kohonen88london,
  author =       "Teuvo Kohonen",
  title =        "Associative Memories and Representations of Knowledge
                 as Internal States in Distributed Systems",
  booktitle =    "European Seminar on Neural Computing, London, U.K.,
                 February 8-9",
  year =         "1988",
  publisher =    "IBC Technical Services Ltd.",
}

@InProceedings{cnlp:kohonen89,
  author =       "Teuvo Kohonen",
  title =        "On the significance of internal representations in
                 neural networks",
  booktitle =    "First IEE International Conference on Artificial
                 Neural Networks, London, UK, October 1989",
  year =         "1989",
  pages =        "1",
  publisher =    "IEE",
}

@InCollection{cnlp:kohonen90eckmiller2,
  author =       "Teuvo Kohonen",
  title =        "Internal Representations and Associative Memory",
  booktitle =    "Parallel Processing in Neural Systems and Computers",
  publisher =    "Elsevier (North-Holland)",
  year =         "1990",
  editor =       "R. Eckmiller and G. Hartman and G. Hauske",
  pages =        "177--182",
}

@InProceedings{cnlp:veelenturf92,
  author =       "L. P. J. Veelenturf",
  title =        "Representation of Spoken Words in a Self-Organizing
                 Neural Net",
  booktitle =    "Twente Workshop on Language Technology 3:
                 {C}onnectionism and Natural Language Processing,
                 Enschede, The Netherlands, May 1992",
  year =         "1992",
  editor =       "Anton Nijholt Marc F. J. Drossaers",
  pages =        "1--4",
  publisher =    "Department of Computer Science, University of Twente",
}

@TechReport{cnlp:ballard86,
  author =       "D. H. Ballard",
  title =        "Interpolation Coding: {A} Representation for Numbers
                 in Neural Nets",
  institution =  "University of Rochester Computer Science Department",
  number =       "TR 175",
  year =         "1986",
  keywords =     "O04 H03 O06",
  abstract =     "also discusses a method of combining evidence in
                 neural nets .br br 30 pages \$1.25",
}

@TechReport{cnlp:feldman86,
  author =       "J. A. Feldman",
  title =        "Neural Representation of Conceptual Knowledge",
  institution =  "University of Rochester Computer Science Department",
  number =       "TR 189",
  year =         "1986",
  keywords =     "AI12 AI16",
  abstract =     "35 pages, \$1.50 .br br discusses holographic models",
}

@Article{cnlp:chrisman,
  author =       "L. Chrisman",
  title =        "Learning Recursive Distributed Representations for
                 Holistic Computation",
  journal =      "Connection Science",
  volume =       "3",
  number =       "4",
  pages =        "345--366",
  year =         "1991",
}

@Article{cnlp:dell,
  author =       "G. S. Dell",
  title =        "Positive Feedback in Hierarchical Connectionist
                 Models: Applications to Language Production",
  journal =      "Cognitive Science",
  volume =       "9",
  number =       "1",
  pages =        "3--24",
  year =         "1985",
}

@PhdThesis{cnlp:dorffner89,
  author =       "G. Dorffner",
  title =        "A Sub-Symbolic Connectionist Model of Basic Language
                 Functions",
  school =       "Indiana University, Dept. of Computer Science",
  year =         "1989",
}

@InCollection{cnlp:dorffner90,
  author =       "G. Dorffner",
  title =        "A Radical View on Connectionist Language Modelling",
  booktitle =    "Konnektionismus in Artificial Intelligence und
                 Kognitionforschung",
  publisher =    "Springer",
  address =      "Berlin",
  pages =        "217--220",
  year =         "1990",
}

@InProceedings{cnlp:hadley,
  author =       "R. Hadley",
  title =        "Compositionality and Systematicity in Connectionist
                 Language Learning",
  booktitle =    "Proceedings of the Fourteenth Annual Conference of the
                 Cognitive Science Society",
  publisher =    LEA,
  address =      LEAaddr,
  pages =        "659--664",
  year =         "1992",
}

@InProceedings{cnlp:hare,
  author =       "M. Hare and J. Elman",
  title =        "A Connectionist Account of English Inflectional
                 Morphology",
  booktitle =    "Proceedings of the Fourteenth Annual Conference of the
                 Cognitive Science Society, Bloomington, IN, July 1992",
  publisher =    LEA,
  pages =        "265--270",
  year =         "1992",
}

@Article{cnlp:karen,
  author =       "L. F. R. Karen",
  title =        "Identification of Topical Entities in Discourse:{A}
                 Connectionist Approach to Attentional Mechanisms in
                 Language",
  journal =      "Connection Science",
  pages =        "103--122",
  year =         "1990",
}

@InProceedings{cnlp:lange92,
  author =       "T. Lange and C. Wharton",
  title =        "{REMIND}: Integrating Language Understanding and
                 Episodic Memory Retrieval in a Connectionist Network",
  booktitle =    "Proceedings of the Fourteenth Annual Conference of the
                 Cognitive Science Society, Bloomington, IN, July 1992",
  publisher =    LEA,
  address =      LEAaddr,
  pages =        "576--581",
  year =         "1992",
}

@InProceedings{cnlp:liberman,
  author =       "M. Liberman",
  title =        "Questions about Connectionist Models of Natural
                 Language",
  booktitle =    "Proceedings of the 24th Annual Meeeting of the
                 Association for Computional Linguistics, New York, NY,
                 July 1986",
  year =         "1986",
}

@InCollection{cnlp:mozer93,
  author =       "M. C. Mozer and S. Das",
  title =        "A Connectionist Symbol Manipulator that Discovers the
                 Structure of Context-Free languages",
  editor =       "S. J. et al. Hanson",
  booktitle =    "Advances in Neural Information Processing Systems 5",
  publisher =    "Morgan Kaufmann",
  pages =        "863--870",
  year =         "1993",
}

@InProceedings{cnlp:waltz86,
  author =       "D. Waltz",
  title =        "Connectionist Models for Natural Language Processing",
  booktitle =    "Proceedings of the 24th Annual Meeting of the
                 Assocation for Computational Linguistics",
  year =         "1986",
}

@Book{cnlp:touretzky91,
  editor =       "D. Touretzky",
  title =        "Connectionist Approaches to Language Learning",
  publisher =    "Kluwer Academic Publishers",
  series =       "The Kluwer International Series in Engineering and
                 Computer Science",
  year =         "1991",
  ISBN =         "0792392167",
}

@InProceedings{cnlp:andersoncw_87,
  author =       "C. W. Anderson",
  title =        "Strategy Learning in Multilayer Connectionist
                 Representations",
  booktitle =    "Proceedings of the Fourth International Workshop on
                 Machine Learning, Irvine, CA, June 1987",
  publisher =    "Morgan Kaufmann",
  pages =        "103--114",
  year =         "1987",
}

@InProceedings{cnlp:elman89b,
  author =       "J. L. Elman",
  title =        "Structured Representations and Connectionist Models",
  booktitle =    "Proceedings of the Eleventh Annual Conference of the
                 Cognitive Science Society",
  publisher =    LEA,
  address =      LEAaddr,
  year =         "1989",
}

@TechReport{cnlp:french91,
  author =       "R. M. French",
  title =        "Using Semi-Distributed Representations to Overcome
                 Catastrophic Forgetting in Connectionist Networks",
  institution =  "Center for Research on Concepts and Cognition, Indiana
                 University",
  number =       "CRCC Tech. Report. 51-1991",
  year =         "1991",
}

@Article{cnlp:french92,
  author =       "R. M. French",
  title =        "Semi-Distributed Representations and Catastrophic
                 Forgetting in Connectionist Networks",
  journal =      "Connection Science",
  volume =       "4",
  number =       "3--4",
  pages =        "365--378",
  year =         "1992",
}

@Article{cnlp:griffith,
  author =       "N. Griffith and P. Todd",
  title =        "Process and Representation in Connectionist Models of
                 Musical Structure",
  journal =      "Connection Science",
  volume =       "6",
  number =       "1--2",
  pages =        "131--134",
  year =         "1994",
}

@InProceedings{cnlp:holbach-weber,
  author =       "S. Holbach-weber",
  title =        "A Connectionist Model of Conceptual Representation",
  booktitle =    "IEEE International Conference On Neural Networks",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  year =         "1989",
}

@InProceedings{cnlp:mozer90,
  author =       "M. C. Mozer",
  title =        "Discovering Faithful 'Wickelfeature' Representations
                 in a Connectionist Network",
  booktitle =    "The Twelfth Annual Conference of the Cognitive Science
                 Society, Cambridge, MA, July 1990",
  publisher =    LEA,
  address =      LEAaddr,
  pages =        "356--363",
  year =         "1990",
}

@InProceedings{cnlp:saund,
  author =       "E. Saund",
  title =        "Abstraction and Representation of Continuous Variables
                 in Connectionist Networks",
  booktitle =    "Proceedings of the 5th National Conference on
                 Artificial Intelligence(AAAI-86)",
  publisher =    "Morgan Kaufmann",
  year =         "1986",
}

@TechReport{cnlp:sharkey92b,
  author =       "N. Sharkey",
  title =        "The Ghost in the Hybrid: {A} study of Uniquely
                 Connectionist Representations",
  institution =  "Computer Science Dept, University of Exeter",
  number =       "228",
  year =         "1992",
}

@InProceedings{cnlp:touretzky85,
  author =       "D. S. Touretzky and G. E. Hinton",
  title =        "Symbols among the Neurons: Details of a Connectionist
                 Inference Architecture",
  booktitle =    "Proceedings of the 8th International Joint Conference
                 on Artificial Intelligence",
  pages =        "238--243",
  publisher =    "Morgan Kaufmann",
  year =         "1985",
}

@Article{cnlp:touretzky88,
  author =       "D. S. Touretzky and Hinton G. E.",
  title =        "A Distributed Connectionist Production System",
  journal =      "Cognitive Science",
  volume =       "12",
  number =       "3",
  pages =        "423--466",
  year =         "1988",
}

@InProceedings{cnlp:jordan86,
  author =       "M. I. Jordan",
  title =        "Attractor Dynamics and Parallelism in a Connectionist
                 Sequential Machine",
  booktitle =    "Proceedings of the 8th Annual Cognitive Science
                 Society Conference",
  publisher =    LEA,
  address =      LEAaddr,
  year =         "1986",
}

@Article{cnlp:elman91,
  author =       "J. L. Elman",
  title =        "Distributed Representations, simple recurrent
                 networks, and grammatical structure",
  journal =      "Machine Learning",
  volume =       "7",
  number =       "2--3",
  pages =        "195--226",
  year =         "1991",
}

@InCollection{cnlp:sharkey92a,
  author =       "N. E. Sharkey and A. J. C. Sharkey",
  title =        "A modular design for connectionist parsing",
  editor =       "F. J. Marc and A. N. Drossaers",
  booktitle =    "Twente Workshop on Language Technology 3:
                 Connectionism and Natural Language Processing,
                 Enschede, The Netherlands, May 1992",
  pages =        "87--96",
  publisher =    "Department of Computer Science, University of Twente",
  year =         "1992",
}

@TechReport{cnlp:stolcke,
  author =       "A. Stolcke",
  title =        "Learning Feature Based Semantics with Simple Recurrent
                 Networks",
  institution =  "International Computer Science Institute",
  address =      "Berkeley, CA",
  number =       "TR-90-015",
  year =         "1990",
}

@Article{cnlp:servan-schrieber,
  author =       "D. Servan-Schrieber and A. Cleeremans and J. L.
                 McClelland",
  title =        "Graded State Machines: The Representation of Temporal
                 Contingencies in Simple Recurrent Networks",
  journal =      "Machine Learning",
  volume =       "7",
  pages =        "161--194",
  year =         "1991",
}

@InProceedings{cnlp:das92,
  author =       "S. Das and C. L. Giles and G. Z. Sun",
  title =        "Learning Context-Free Grammars: Capabilities and
                 Limitations of a Recurrent Neural Network with an
                 External Stack Memory",
  booktitle =    "Proceedings of the Fourteenth Annual Conference of the
                 Cognitive Science Society",
  year =         "1992",
}

@InCollection{cnlp:das94,
  author =       "S. Das and Mozer M. C.",
  title =        "A Hybrid Gradient-Descent/Clustering Technique for
                 Finite State Machine Induction",
  editor =       "J. D. Cowan and G. Tesauro and J. Alspector",
  booktitle =    "Advances in Neural Information Processing Systems 6",
  publisher =    "Morgan Kaufmann",
  pages =        "19--26",
  year =         "1994",
}

@Article{cnlp:stjohn89,
  author =       "James L. McClelland and Mark {St. John} and Roman
                 Taraban",
  title =        "Sentence Comprehension: {A} Parallel Distributed
                 Processing Approach",
  journal =      "Language and Cognitive Processes",
  year =         "1989",
  volume =       "3/4",
  pages =        "287--335",
}

@Article{cnlp:ward-aij,
  author =       "Nigel Ward",
  title =        "A Parallel Approach to Syntax for Generation",
  year =         "1992",
  volume =       "57",
  pages =        "183--225",
  journal =      "Artificial Intelligence",
  abstract =     "To produce good utterances from nontrivial inputs a
                 natural langauge generator should consider many words
                 in parallel, which raises the question of how to handle
                 syntax in a parallel generator. If a generator is
                 incremental and centered on the task of word choice,
                 then the role of syntax is merely to help evaluate the
                 appropriateness of words. One way to do this is to
                 represent syntactic knowledge as an inventory of
                 ``syntactic constructions'' and to have many
                 constructions active in parallel at run-time. If this
                 is done then the syntactic form of utterances can be
                 emergent, resulting from synergy among constructions,
                 and there is no need to build up or manipulate
                 representations of syntactic structure. This approach
                 is implemented in FIG, an incremental generator based
                 on spreading activation, in which syntactic knowledge
                 is represented in the same network as world knowledge
                 and lexical knowledge.",
}

@InCollection{cnlp:smythe,
  author =       "E. J. Smythe",
  title =        "Temporal Representations in a Connectionist Speech
                 System",
  editor =       "D. S. Touretzky",
  booktitle =    "Advances in Neural Information Processing Systems",
  publisher =    "Morgan Kaufmann",
  pages =        "240--247",
  year =         "1989",
}

@TechReport{cnlp:thornton93,
  author =       "C. Thornton",
  title =        "How Much is Enough? {A} Connectionist Perspective on
                 the Representation Debate",
  institution =  "School of Cognitive and Computing Sciences",
  addresss =     "University of Sussex, UK",
  number =       "274",
  year =         "1993",
}

@InCollection{cnlp:touretzky90a,
  author =       "D. S. Touretzky and G. Elvgren",
  title =        "Rule Representations in a Connectionist Chunker",
  editor =       "D. S. Touretzky",
  booktitle =    "Advances in Neural Information Processing Systems 2",
  publisher =    "Morgan Kaufmann",
  pages =        "431--438",
  year =         "1990",
}

@InProceedings{cnlp:mozer93a,
  author =       "Michael C. Mozer and Sreerupa Das",
  title =        "A Connectionist Chunker that Induces the Structure of
                 Context-Free Languages",
  booktitle =    "Advances in Neural Information Processing Systems 5",
  year =         "1993",
  address =      "Denver, CO",
}

@TechReport{cnlp:pinker87,
  author =       "S. Pinker and A. Prince",
  title =        "On Language and Connectionism: Analysis of a Parallel
                 Distributed Processing Model of Language Acquisition",
  number =       "Occasional Paper \#33",
  institution =  "MIT Center for Cognitive Science",
  year =         "1987",
  comment =      "Critique of Rumelhart and McClelland's connectionist
                 approach to learning English past tense construction.",
}

@Article{cnlp:dell88,
  author =       "G. S. Dell",
  title =        "The retrieval of phonological forms in production:
                 Test of predictions from a connectionist model",
  journal =      "Journal of Memory and Language",
  year =         "1988",
  volume =       "27",
  pages =        "124--142",
}

@InProceedings{cnlp:gasser88,
  author =       "Michael E. Gasser and Michael G. Dyer",
  title =        "Sequencing in a connectionist model of language
                 processing",
  volume =       "1",
  pages =        "185--190",
  booktitle =    "Proceedings of COLING88, International Conference on
                 Computational Linguistics, Budapest, August 1988",
  year =         "1988",
}

@InCollection{cnlp:tanenhaus87,
  author =       "M. K. Tanenhaus and G. S. Dell and G. Carlson",
  address =      "Cambridge, MA",
  booktitle =    "Modularity in Knowledge Representation and
                 Natural-Language Understanding",
  chapter =      "4",
  editor =       "J. L. Garfield",
  pages =        "82--108",
  publisher =    "The MIT Press",
  title =        "Context Effects in Lexical Processing: {A}
                 Connectionist Approach to Modularity",
  year =         "1987",
}

@Article{cnlp:waltrous91,
  author =       "R. L. Waltrous",
  journal =      "Computer Speech and Language",
  number =       "5",
  pages =        "341--362",
  title =        "Context-modulated vowel discrimination using
                 connectionist networks",
  year =         "1991",
  notes =        "context modulation",
}

@InProceedings{cnlp:wu90,
  author =       "Lizhong Wu and Frank Fallside",
  title =        "The optimal gain sequence for fastest learning in
                 connectionist vector quantiser design",
  booktitle =    "Proceedings of the International Conference on Spoken
                 Language Processing, Kobe, Japan",
  year =         "1990",
  pages =        "1029--1032",
}

@InProceedings{cnlp:berg87,
  title =        "A Parallel Natural Language Processing Architecture
                 with Distributed Control",
  author =       "G. Berg",
  year =         "1987",
  pages =        "487--495",
  booktitle =    "Proceedings of the Ninth Annual Cognitive Science
                 Society, Seattle, WA, July 1987",
  organization = "Cognitive Science Society",
  publisher =    LEA,
  address =      LEAaddr,
  note =         "Keywords: ``Autonomous'' semantic networks, spreading
                 activation. Not connectionism, but could be interesting
                 to connectionists.",
}

@InCollection{cnlp:kukich87,
  author =       "Karen Kukich",
  title =        "Where Do Phrases Come From: {S}ome Preliminary
                 Experiments in Connectionist Phrase Generation",
  booktitle =    "Natural Language Generation: {N}ew Results in
                 Artificial Intelligence",
  publisher =    "Kluwar Academic Press",
  year =         "1987",
  editor =       "Gerard Kempen",
  chapter =      "26",
  pages =        "405--421",
  address =      "Boston",
}

@PhdThesis{cnlp:pollack87b,
  title =        "On Connectionist Models of Natural Language
                 Processing",
  author =       "Jordan B. Pollack",
  year =         "1987",
  school =       "Computer Science Department of the University of
                 Illinois at Urbana-Champaign",
  note =         "Also available at technical report MCCS-87-100 from
                 Box 3CRL, Computing Research Laboratory, New Mexico
                 State University, Las Cruces, NM 88003",
}

@InProceedings{cnlp:bookman87,
  title =        "A Microfeature Based Scheme for Modelling Semantics",
  author =       "Lawrence A. Bookman",
  year =         "1987",
  booktitle =    "Proceedings of the Tenth International Joint
                 Conference on Artifical Intelligence, Milan, Italy,
                 August 1987",
  organization = "AAAI",
  publisher =    "Morgan Kaufmann",
  address =      "Los Altos, CA",
  keywords =     "context setting, massively parallel networks,
                 microfeatures, word sense disambiguation",
}

@InProceedings{cnlp:chun87a,
  title =        "A model of schema selection using marker passing and
                 connectionist spreading activation",
  author =       "Hon Wai Chun and Alejandro Mimo",
  year =         "1987",
  pages =        "887--895",
  booktitle =    "Proceedings of the Ninth Annual Cognitive Science
                 Society Conference, Seattle, WA, June 1987",
  organization = "Cognitive Science Society",
  publisher =    LEA,
  address =      LEAaddr,
}

@InProceedings{cnlp:charniak87,
  title =        "A connectionist context-free parser which is not
                 context-free, but then it is not really connectionist
                 either",
  author =       "E. Charniak and E. Santos",
  year =         "1987",
  pages =        "70--77",
  booktitle =    "Proceedings of the Ninth Annual Cognitive Science
                 Society Conference, Seattle, WA, July 1987",
  organization = "Cognitive Science Society",
  publisher =    LEA,
  address =      LEAaddr,
}

@PhdThesis{cnlp:cottrell85,
  title =        "A Connectionist Approach to Word Sense
                 Disambiguation",
  author =       "Garrison Weeks Cottrell",
  year =         "1985",
  school =       "Computer Science Department, University of Rochester",
  note =         "Available as TR-154",
}

@InProceedings{cnlp:derthick87a,
  title =        "A Connectionist Architecture for Representing and
                 Reasoning about Structured Knowledge",
  author =       "M. Derthick",
  year =         "1987",
  booktitle =    "Proceedings of the Ninth Annual Cognitive Science
                 Society Conference, Seattle, WA, July 1987",
  organization = "Cognitive Science Society",
  publisher =    LEA,
  address =      LEAaddr,
}

@InProceedings{cnlp:dolan87,
  title =        "Symbolic Schemata, Role Binding, and the Evolution of
                 Structure in Connectionist Memories",
  author =       "Charles P. Dolan and Michael G. Dyer",
  pages =        "II:287--298",
  year =         "1987",
  booktitle =    "Proceedings of the IEEE First International Conference
                 on Neural Networks, San Diego, CA, June 1987",
  organization = "IEEE",
}

@TechReport{cnlp:jordan86a,
  title =        "Serial Order: {A} Parallel Distributed Processing
                 Approach",
  author =       "Michael I. Jordan",
  year =         "1986",
  institution =  "Institute for Cognitive Science, University of
                 California at San Diego",
  number =       "ICS-8604",
  address =      "La Jolla, CA",
}

@InProceedings{cnlp:kalita87,
  title =        "Generation of Simple Sentences in {E}nglish Using the
                 Connectionist Model of Computation",
  author =       "Jugal Kalita and Lokendra Shastri",
  year =         "1987",
  pages =        "555--565",
  booktitle =    "Proceedings of the Ninth Annual Cognitive Science
                 Society Conference, Seattle, WA, July 1987",
  organization = "Cognitive Science Society",
  publisher =    LEA,
  address =      LEAaddr,
}

@TechReport{cnlp:lachter87,
  title =        "The Relation Between Linguistic Structure and Theories
                 of Language Learning --- {A} Constructive Critique of
                 Some Connectionist Learning Models",
  author =       "Joel Lachter and Thomas Bever",
  year =         "1987",
  institution =  "University of Rochester",
  address =      "Rochester, NY 14627",
  number =       "Cognitive Science Technical Report URCS 44",
  note =         "Also published in Cognition, Volume 28, Numbers 1-2,
                 March 1988",
}

@InProceedings{cnlp:miyata87,
  title =        "Organization of Action Sequences in Motor Learning:
                 {A} Connectionist Approach",
  author =       "Yoshiro Miyata",
  year =         "1987",
  booktitle =    "Proceedings of the Ninth Annual Cognitive Science
                 Society Conference, Seattle, WA, July 1987",
  organization = "Cognitive Science Society",
  publisher =    LEA,
  address =      LEAaddr,
  anote =        "Learning by chunking and action hierarchy building",
}

@InProceedings{cnlp:parisi87,
  title =        "Connectionist Modelling of Syntactic Constraints on
                 Sentence Processing",
  author =       "Domenico Parisi and Stefano Nolfi",
  pages =        "II:507--512",
  year =         "1987",
  booktitle =    "Proceedings of the IEEE First International Conference
                 on Neural Networks",
  organization = "IEEE",
}

@InProceedings{cnlp:norrod87,
  title =        "Feedback-Induced Sequentiality in Neural Networks",
  author =       "Forrest E. Norrod and Michael C. {O'Neill} and Erann
                 Gat",
  pages =        "II:251--258",
  year =         "1987",
  booktitle =    "Proceedings of the IEEE First International Conference
                 on Neural Networks",
  organization = "IEEE",
}

@InProceedings{cnlp:ratcliff87,
  title =        "Order Information and Distributed Memory Models",
  author =       "Roger Ratcliff",
  year =         "1987",
  booktitle =    "Proceedings of the Ninth Annual Cog. Sci. Soc.
                 Conference",
  organization = "Cog. Sci. Soc.",
  publisher =    LEA,
  address =      LEAaddr,
  note =         "Experimental results used to derive contraints for
                 models of memory and sequential behavior, including
                 connectionist models.",
}

@InProceedings{cnlp:rosenberg87,
  title =        "Revealing the Structure of {NET}talk's Internal
                 Representations",
  author =       "Charles R. Rosenberg",
  year =         "1987",
  pages =        "537--554",
  booktitle =    "Proceedings of the Ninth Annual Cog. Sci. Soc.
                 Conference",
  organization = "Cog. Sci. Soc.",
  publisher =    LEA,
  address =      LEAaddr,
}

@Article{cnlp:rumelhart81,
  title =        "An Interactive Activation Model of Context Effects in
                 Letter Perception: {P}art 2. {T}he Contextual
                 Enhancement Effect and Some Tests and Extensions of the
                 Model",
  author =       "David E. Rumelhart and James L. McClelland",
  journal =      "Psychological Review",
  year =         "1982",
  pages =        "60--94",
  volume =       "89",
}

@InProceedings{cnlp:samad86,
  title =        "A Connectionist Network That Learns to Process Some
                 (Very) Simple Sentences",
  author =       "Tariq Samad",
  year =         "1986",
  booktitle =    "ESCOL-86: The Proceedings of the Third Eastern States
                 Conference on Linguistics",
  publisher =    "Department of Linguistics, Ohio State University",
  note =         "Simulates sequential processing in the same way that
                 Sejnowski's NETtalk does.",
}

@InProceedings{cnlp:sharkey86,
  title =        "Mixing Binar and Continuous Connection Schemes for
                 Knowledge Access",
  author =       "N. E. Sharkey and R. F. E. Sutcliffe and W. R.
                 Wobcke",
  year =         "1986",
  booktitle =    "Proceedings of the Sixth National Conference on
                 Artificial Intelligence",
  organization = "American Association for Artificial Intelligence",
  publisher =    "Morgan Kaufmann",
  address =      "Los Altos, CA",
}

@InCollection{cnlp:sharkey87,
  title =        "{KAN}: {A} knowledge access network",
  author =       "N. E. Sharkey and A. J. C. Sharkey",
  year =         "1987",
  booktitle =    "Communication failure in dialogue and discourse",
  editor =       "Ronan G. Reilly",
  publisher =    "North-Holland",
  address =      "Amsterdam",
}

@InProceedings{cnlp:smolensky87b,
  title =        "On the Connectionist Reduction of Conscious Rule
                 Interpretation",
  author =       "Paul Smolensky",
  year =         "1987",
  pages =        "187--194",
  booktitle =    "Proceedings of the Ninth Annual Cognitive Science
                 Society Conference",
  organization = "Cognitive Science Society",
  publisher =    LEA,
  address =      LEAaddr,
}

@InProceedings{cnlp:stjohn87,
  title =        "Reconstructive Memory for Sentences: {A} {PDP}
                 Approach",
  author =       "Mark F. St. John and James L. McClelland",
  year =         "1987",
  editor =       "Danny R. Moates and Richard Butrick",
  booktitle =    "Proceedings of Inference OUIC 86",
  publisher =    "Ohio University",
}

@InProceedings{cnlp:sutton85,
  title =        "The Learning of World Models by Connectionist
                 Networks",
  author =       "Richard S. Sutton and Brian Pinette",
  year =         "1985",
  booktitle =    "Proceedings of the Seventh Annual Cognitive Science
                 Society Conference",
  organization = "Cognitive Science Society",
  publisher =    LEA,
  address =      LEAaddr,
}

@InProceedings{cnlp:touretzky86b,
  title =        "Representing and Transforming Recursive Objects in a
                 Neural Network, or ``{T}rees {\it do} Grow on
                 {B}oltzmann Machines''",
  author =       "David S. Touretzky",
  year =         "1986",
  booktitle =    "Proceedings of the 1986 IEEE International Conference
                 on Systems, Man, and Cybernetics",
  organization = "IEEE",
  note =         "Atlanta, GA",
}

@TechReport{cnlp:touretzky86c,
  title =        "A Distributed Connectionist Production System",
  author =       "David S. Touretzky and Geoffrey E. Hinton",
  year =         "1986",
  institution =  "Department of Computer Science, Carnegie-Mellon
                 University",
  number =       "CMU-CS-86-172",
  address =      "Pittsburgh, PA",
}

@InProceedings{cnlp:touretzky87a,
  title =        "A Distributed Connectionist Representation for Concept
                 Structures",
  author =       "David S. Touretzky and Shai Geva",
  year =         "1987",
  booktitle =    "Proceedings of the Ninth Annual Cognitive Science
                 Society Conference",
  organization = "Cognitive Science Society",
  publisher =    LEA,
  address =      LEAaddr,
}

@InProceedings{cnlp:touretzky87b,
  title =        "Representing Conceptual Structures in a Neural
                 Network",
  author =       "David S. Touretzky",
  pages =        "II:279--286",
  year =         "1987",
  booktitle =    "Proceedings of the IEEE First International Conference
                 on Neural Networks",
  organization = "IEEE",
}

@InProceedings{cnlp:touretzky87c,
  title =        "Symbol Structures in Connectionist Networks: Five
                 Properties and Two Architectures",
  author =       "David S. Touretzky and Mark A. Derthick",
  year =         "1987",
  booktitle =    "Proceedings of the IEEE COMPCON",
  organization = "IEEE",
}

@InProceedings{cnlp:waltz87,
  title =        "Connectionist Models: Not Just a Notational Variant,
                 Not a Panacea",
  author =       "David L. Waltz",
  year =         "1987",
  pages =        "58--64",
  booktitle =    "Proceedings of the Third Workshop on Theoretical
                 Issues in Natural Language Processing(TINLAP-3)",
  publisher =    "Association for Computational Linguistics",
  note =         "Abstract: Connectionist models inherently include
                 features and exhibit behaviors which are difficult to
                 achieve with traditional logic-based models. Among the
                 more important characteristics are: (1) the ability to
                 compute nearest match rather than requiring unification
                 or exact match; (2) learning; (3) fault tolerance
                 through the integration of overlapping modules, each of
                 which may be incomplete or fallible, and (4) the
                 possibility of scaling up such systems by many orders
                 of magnitude, to operate more rapidly or to handle much
                 larger problems, or both. However, it is unlikely that
                 connectionist models will be able to learn all of
                 language from experience, because it is unlikely that a
                 full cognitive system could be built via learning from
                 an initially random network; any successful large-scale
                 connectionist learning system will have to be to some
                 degree ``genetically'' prewired.",
}

@InCollection{cnlp:hinton81,
  author =       "G. E. Hinton",
  title =        "Implementing Semantic Nets in Parallel Hardware",
  booktitle =    "Parallel Models of Associative Memory",
  editor =       "G. E. Hinton and J. A. Anderson",
  publisher =    LEA,
  address =      LEAaddr,
  year =         "1981",
}

@Article{cnlp:cottrell83,
  author =       "G. W. Cottrell and S. L. Small",
  title =        "A connectionist scheme for modeling word-sense
                 disambiguation",
  journal =      "Cognition and Brain Theory",
  volume =       "6",
  pages =        "89--120",
  year =         "1983",
}

@InProceedings{cnlp:palmer94a,
  author =       "David D. Palmer and Marti A. Hearst",
  title =        "Adaptive Sentence Boundary Disambiguation",
  booktitle =    "Proceedings of the Fourth ACL Conference on Applied
                 Natural Language Processing (13--15 October 1994,
                 Stuttgart)",
  publisher =    "Morgan Kaufmann",
  pages =        "78--83",
  year =         "1994",
}

@MastersThesis{cnlp:palmer94b,
  author =       "David D. Palmer",
  title =        "{SATZ} - An Adaptive Sentence Segmentation System",
  booktitle =    "Master's Thesis, University of California at Berkeley,
                 December 1994, available as UCB Technical Report
                 CSD-94-846",
  school =       "University of California at Berkeley",
  year =         "1994",
}

@Article{cnlp:sun92,
  author =       "R. Sun",
  title =        "On Variable Binding in Connectionist Networks",
  journal =      "Connection Science",
  volume =       "4",
  number =       "2",
  pages =        "93--124",
  year =         "1992",
}

@TechReport{cnlp:sun90,
  author =       "R. Sun",
  title =        "Integrating Rules and Connectionism for Robust
                 Reasoning",
  institution =  "Brandeis University, Computer Science Dept",
  number =       "TRCS-90-154",
  year =         "1990",
}

@Unpublished{cnlp:sun95a,
  author =       "R. Sun",
  title =        "Robust Reasoning: integrating Rule-based and
                 Similarity-based Reasoning",
  note =         "To be published in {\em Artificial Intelligence}",
  year =         "1995",
}

@TechReport{cnlp:stolcke89a,
  author =       "A. Stolcke",
  title =        "A Connectionist Model of Unification",
  institution =  "International Computer Science Institute",
  number =       "TR-89-032",
  year =         "1989",
}

@InProceedings{cnlp:stolcke89b,
  author =       "A. Stolcke",
  title =        "Processing Unification-Based Grammars in a
                 Connectionist Framework",
  booktitle =    "Proceedings of the Eleventh Annual Conference of the
                 Cognitive Science Society",
  publishers =   LEA,
  address =      LEAaddr,
  pages =        "908--915",
  year =         "1989",
}

@Book{cnlp:wermter95,
  author =       "S. Wermter",
  title =        "Hybrid Connectionist Natural Language Processing",
  publisher =    "Chapman and Hall",
  year =         "1995",
}

@InProceedings{cnlp:wermter94,
  author =       "S. Wermter",
  title =        "Learning natural language filtering under noisy
                 conditions",
  address =      "San Antonio, USA",
  booktitle =    "Proceedings of the Tenth IEEE Conference on Artificial
                 Intelligence for Applications",
  pages =        "215--221",
  year =         "1994",
}

@InProceedings{cnlp:wermter94a,
  author =       "S. Wermter and M. L{\"o}chel",
  title =        "Connectionist learning of flat syntactic analysis for
                 speech/language systems",
  address =      "Sorrento, Italy",
  booktitle =    "Proceedings of the International Conference on
                 Artificial Neural Networks",
  pages =        "941--944",
  year =         "1994",
}

@InProceedings{cnlp:wermter94b,
  author =       "S. Wermter and V. Weber",
  title =        "Learning Fault-tolerant Speech Parsing with {SCREEN}",
  address =      "Seattle, USA",
  booktitle =    "Proceedings of the National Conference on Artificial
                 Intelligence",
  pages =        "670--675",
  year =         "1994",
}

@InProceedings{cnlp:weber95,
  author =       "V. Weber and S. Wermter",
  title =        "Towards Understanding Spontaneous Dialog Utterances in
                 a Hybrid Model",
  address =      "Sheffield, UK",
  booktitle =    "Proceedings of the Tenth Biennial Conference on
                 Artificial Intelligence and Cognitive Science",
  pages =        "(to appear)",
  year =         "1995",
}

@InCollection{cnlp:kwasny92b,
  author =       "S. C. Kwasny and K. A. Faisal",
  title =        "Symbolic parsing via Sub-Symbolic Rules",
  editor =       "J. Dinsmore",
  booktitle =    "Symbolic and Connectionist Paradigms: Closing the
                 Gap",
  publisher =    LEA,
  address =      LEAaddr,
  year =         "1992",
}

@Article{cnlp:kwasny91b,
  author =       "S. C. Kwasny and K. A. Faisal",
  title =        "Rule-based training of neural networks",
  journal =      "Expert systems with Applications (Special Issue on
                 Applying Artificial Neural Networks to Expert
                 Systems)",
  volume =       "2",
  number =       "1",
  pages =        "47--58",
  year =         "1991",
}

@InProceedings{cnlp:kwasny93a,
  author =       "S. C. Kwasny and B. L. Kalman and N. Chang",
  title =        "Distributed Patterns as Hierarchical Structures",
  booktitle =    "Proceedings of the World Congress on Neural Networks,
                 Portland, OR, July 1993",
  volume =       "2",
  pages =        "198--201",
  year =         "1993",
}

@InProceedings{cnlp:kwasny93b,
  author =       "S. C. Kwasny and S. Johnson and B. L. Kalman",
  title =        "An Adaptive Neural Network Parser.",
  editor =       "C. Daglu and B. R. Burke and B. R. Fernandez and J.
                 Ghosh",
  booktitle =    "Artificial Neural Networks in Engineering (ANNIE '93),
                 Intelligent Engineering Systems through Artificial
                 Neural Networks, St Louis, MO, November 1993",
  volume =       "3",
  publisher =    "ASME Press",
  pages =        "467--472",
  year =         "1993",
}

@InProceedings{cnlp:kwasny92c,
  author =       "S. C. Kwasny and B. L. Kalman",
  title =        "A recurrent deterministic parser",
  booktitle =    "Proceedings of the 4th Midwest Artificial Intelligence
                 and Cognitive Science Society Conference",
  year =         "1992",
}

@TechReport{cnlp:kalman94,
  author =       "B. L. Kalman and S. C. Kwasny",
  title =        "High Performance Training of Feedforward and Simple
                 Recurrent Networks",
  institution =  "St. Louis: Department of Computer Science, Washington
                 University",
  number =       "WUCS-94-29",
  year =         "1994",
}

@Article{cnlp:kwasny95,
  author =       "S. C. Kwasny and B. L. Kalman",
  title =        "Tail-Recursive Distributed Representations and Simple
                 Recurrent Networks",
  journal =      "Connection Science",
  volume =       "7",
  number =       "1",
  pages =        "61--80",
  year =         "1995",
}

@InCollection{cnlp:smolensky95,
  author =       "P. Smolensky",
  title =        "Reply: Constituent Structure and Explanation in an
                 Integrated Connectionist/Symbolic Cognitive
                 Architecture",
  booktitle =    "Connectionism: Debates in Psychological Explanation",
  editor =       "C. MacDonald and G. MacDonald",
  publisher =    "Blackwell",
  address =      "Oxford, UK",
  pages =        "223--290",
  year =         "1995",
}

@Book{cnlp:macdonald95,
  editor =       "C. MacDonald and G. MacDonald",
  title =        "Connectionism: Debates in Psychological Explanation",
  publisher =    "Blackwell",
  address =      "Oxford",
  year =         "1995",
}

@Book{cnlp:sun95b,
  editor =       "R. Sun and L. Bookman",
  title =        "Computational Architectures Integrating Neural and
                 Symbolic Processes",
  publisher =    "Kluwer Academic",
  year =         "1995",
}

@InProceedings{cnlp:holldobler90,
  author =       "S. Holldobler",
  title =        "A Structured Connectionist Unification Algorithm",
  booktitle =    "Proceedings of the AAAI National Conference on
                 Artificial Intelligence",
  pages =        "587--593",
  year =         "1990",
}

@TechReport{cnlp:holldobler91,
  author =       "S. Holldobler and F. Kurfess",
  title =        "{CHCL} --- {A} Connectionist Inference System",
  institution =  "International Computer Science Institute",
  number =       "TR-91-052",
  year =         "1991",
}

@Article{cnlp:browne94,
  author =       "A. Browne and J. Pilkington",
  title =        "Unification Using a Distributed Representation",
  journal =      "SIGART Bulletin",
  volume =       "5",
  number =       "1",
  pages =        "33--42",
  year =         "1994",
}

@Article{cnlp:frixone92,
  author =       "M. Frixone and G. Spinelli",
  title =        "Connectionism and Functionalism: the importance of
                 being a sybsymbolist",
  journal =      "Journal of Experimental and Theoretical Artificial
                 Intelligence",
  volume =       "4",
  number =       "1",
  pages =        "3--17",
  year =         "1992",
}

@InProceedings{cnlp:shultz94,
  author =       "T. R. Shultz and D. Buckingham and Y. Oshima-Takae",
  editor =       "S. J. Hanson and T. Petsihe and M. Kearns and R. K.
                 Rivest",
  title =        "A connectionist model of the learning of personal
                 pronouns in English",
  booktitle =    "Computational Learning Theory and Natural Learning
                 Systems: Intersections between theory and experiment
                 --- Proceedings of the Workshop, Berkeley, 1991, CA",
  volume =       "2",
  pages =        "347--362",
  publisher =    "MIT Press",
  address =      "Cambridge, MA",
  year =         "1994",
}

@Book{cnlp:niklasson94,
  editor =       "L. F. Niklasson and M. B. Bod\`{e}n",
  title =        "Connectionism in a broad perspective -- selected
                 papers from the Swedish Conference (University of
                 Skovde, Sweden, 9--10 September 1992)",
  publisher =    "Ellis Horwood",
  address =      "Chichester, UK",
  year =         "1994",
}

@PhdThesis{cnlp:park93,
  author =       "J. J. Park",
  title =        "Neural Network Processing of Linguistic Symbols using
                 a multi-level grading rule",
  school =       "Florida State University",
  address =      "Tallahassee",
  year =         "1993",
}

@PhdThesis{cnlp:balogh94,
  author =       "Imre L. Balogh",
  title =        "An analysis of a connectionist internal
                 representation: {D}o {RAAM} networks produce truly
                 distributed representations?",
  school =       "New Mexico State University",
  address =      "Las Cruces, NM",
  year =         "1994",
}

@PhdThesis{cnlp:daugherty,
  author =       "K. G. Daugherty",
  title =        "Connectionist Inflectional Morphology: a network based
                 account of the past tense",
  school =       "University of Southern California",
  address =      "Los Angeles",
  year =         "1994",
}

@Article{cnlp:dorffner95,
  author =       "G. Dorffner",
  title =        "On grounding language with neural networks",
  journal =      "IEE Colloquium: Grounding Representations ---
                 Integration of Sensory Information in NLP (AI and
                 Neural Nets Digest)",
  number =       "1995/103",
  pages =        "471--473",
  publisher =    "IEE, London, UK",
  year =         "1995",
}

@InProceedings{cnlp:hester94,
  author =       "K. A. Hester and M. J. Bringmann and D. Longan and M.
                 J. Niccolai and W. J. Nowack",
  title =        "The {P}redictive {RAAM}: a {RAAM} that can learn to
                 distinguish sequences from a continuous input stream",
  booktitle =    "WCNN'94: International Neural Networks Society, Annual
                 Meeting, San Diego, June 1994",
  publisher =    LEA,
  address =      LEAaddr,
  volume =       "4",
  pages =        "97--103",
  year =         "1994",
}

@InProceedings{cnlp:legendre91,
  author =       "G. Legendre and Y. Miyata and P. Smolensky",
  title =        "Distributed Recursive Structure Processing",
  booktitle =    "Proceedings of the Scandinavian Conference on
                 Artificial Intelligence, Roskilde, Denmark, May 1991",
  year =         "1991",
  pages =        "47--51",
}

@Article{cnlp:schmidhuber92,
  author =       "J. Schmidhuber",
  title =        "Learning Complex Extended Sequences using the
                 principle of history compression",
  journal =      "Neural Computing",
  volume =       "4",
  number =       "2",
  pages =        "234--242",
  year =         "1992",
}

@InProceedings{cnlp:kamimura91,
  author =       "R. Kamimura",
  title =        "Recurrent Neural Network to acquire the grammatical
                 competence",
  booktitle =    "Proceedings of IJCNN'91, Seattle, July 8-12 1991",
  pages =        "361--366",
  volume =       "1",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  year =         "1991",
}

@InProceedings{cnlp:han91,
  author =       "Youg S. Han and Hyun-Soo Yoon and Key Sun Choi",
  title =        "Cognitive Knowledge Representation in Neural Nets",
  booktitle =    "Proceedings of ACNN'91, Sydney, Australia, Feb 1991",
  year =         "1991",
  pages =        "293",
}

@InProceedings{cnlp:nwana92,
  author =       "H. S. Nwana",
  title =        "Drawing from the shortcomings of Artificial
                 Intelligence: some critical issues Connectionism must
                 address.",
  booktitle =    "Proceedings of EXPERSYS-92",
  address =      "Houston Texas, USA and Paris, France",
  year =         "1992",
  pages =        "495--500",
}

@Article{cnlp:towell94,
  author =       "G. G. Towell and J. W. Shavlik",
  title =        "Knowledge-based Artificial Neural Networks",
  journal =      "Artificial Intelligence",
  volume =       "70",
  number =       "1--2",
  pages =        "119--165",
  year =         "1994",
}

@InProceedings{cnlp:miikkulainen94,
  author =       "R. Miikkulainen",
  title =        "Integrated Connectionist Models: building {AI} systems
                 on subsymbolic foundations",
  booktitle =    "Proceedings of the Sixth International Conference on
                 Tools with Artificial Intelligence, New Orleans, LA,
                 November 1994",
  publisher =    "IEEE Computer Society Press",
  address =      "Los Alamitos, CA",
  pages =        "231--232",
  year =         "1994",
}

@InProceedings{cnlp:archambault94,
  author =       "D. Archambault and J. C. Bassano",
  title =        "A neural network for supervised learning of natural
                 language grammar",
  booktitle =    "Proceedings of the Sixth International Conference on
                 Tools with Artificial Intelligence, New Orleans, LA,
                 November, 1994",
  publisher =    "IEEE Computer Society Press",
  address =      "Los Alamitos, CA",
  pages =        "267--273",
  year =         "1994",
}

@Article{cnlp:manolois94,
  author =       "P. Manolois",
  title =        "First-order recurrent neural networks and
                 deterministic finite-state automata",
  journal =      "Neural Computation",
  volume =       "6",
  number =       "6",
  year =         "1994",
}

@Article{cnlp:perez94,
  author =       "P. Perez and D. Salinas",
  title =        "Storage of Structured Patterns in a neural network",
  journal =      "Physical Review E",
  volume =       "50",
  number =       "5",
  pages =        "4182--4186",
  year =         "1994",
}

@InProceedings{cnlp:ultsch94,
  author =       "A. Ultsch and G. Guinaraes and V. Weber",
  title =        "Self-organising feature maps for logical unification",
  booktitle =    "Moving Towards Expert Systems Globally in the 21st
                 Century",
  address =      "Lisbon/Estoril, Portugal",
  publisher =    "Elmsford, NY, USA",
  pages =        "1288--1294",
  year =         "1994",
}

@Article{cnlp:idicula94,
  author =       "S. M. Idicula",
  title =        "A Connectionist Approach for Communicating with
                 Databases in Natural Language",
  journal =      "Adv. Model. Anal. B",
  volume =       "31",
  number =       "2",
  pages =        "31--40",
  year =         "1994",
}

@InProceedings{cnlp:lonardi94,
  author =       "S. Lonardi and A. Sperduti and A. Starita",
  title =        "Encoding pyramids by {L}abeling {RAAM}",
  booktitle =    "Neural Networks for Signal Processing IV: Proceedings
                 of the 1994 IEEE Workshop",
  address =      "Ermioni, Greece",
  publisher =    "IEEE, New York",
  pages =        "423--430",
  year =         "1994",
}

@InProceedings{cnlp:craven93,
  author =       "M. W. Craven and J. W. Shavlik",
  title =        "Extracting Symbolic Rules from Artificial Neural
                 Networks",
  booktitle =    "Proceedings of the Second International Workshop on
                 Multistrategy Learning --- MSL-93, Harpers Ferry, WV,
                 May 1993",
  pages =        "207--217",
  year =         "1993",
}

@InProceedings{cnlp:boyd93,
  author =       "R. Boyd and J. Driscoll and I. Syv",
  title =        "Incorporating Semantics within a Connectionist Model
                 and a Vector Processing Model",
  booktitle =    "Second Text REtrieval Conference -- TREC-2,
                 Gaithensburg, MD, August 1993",
  pages =        "291--302",
  year =         "1993",
}

@InProceedings{cnlp:cohen95,
  author =       "M. E. Cohen and D. L. Hudson and M. F. Anderson",
  title =        "Integration of symbolic reasoning and neural network
                 modelling for reasoning with uncertain information",
  booktitle =    "Proceedings of the ISCA International Conference,
                 Fourth Golden West Conference on Intelligent Systems",
  address =      "San Francisco, CA, USA",
  publisher =    "Raleigh, NC, USA: International Society for Computers
                 and their Applications",
  pages =        "179--183",
  year =         "1995",
}

@InProceedings{cnlp:wermter94c,
  author =       "S. Wermter and M. Lochel",
  title =        "Connectionist learning of flat syntactic analysis for
                 speech/language systems",
  booktitle =    "Proceedings of the International Conference on
                 Artificial Neural Networks --- ICANN94",
  address =      "Sorrento, Italy",
  publisher =    "Springer Verlag, Berlin, Germany",
  pages =        "941--944",
  volume =       "2",
  year =         "1994",
}

@InProceedings{cnlp:browne94a,
  author =       "A. Browne and J. Pilkington",
  title =        "Variable binding in a neural network using a
                 distributed representation",
  booktitle =    "Proceedings of the European Symposion on Artificial
                 Neural Networks --- ESANN, Brussels, Belgium, April
                 1994",
  pages =        "199--204",
  year =         "1994",
}

@InProceedings{cnlp:soler94,
  author =       "O. Soler and R. Van Hoe",
  title =        "Bar: {A} connectionist model of bi-lingual access
                 representations",
  booktitle =    "Proceedings of the International Conference on
                 Artificial Neural Networks --- ICANN94",
  address =      "Sorrento, Italy",
  publisher =    "Springer Verlag, Berlin, Germany",
  pages =        "263--267",
  volume =       "1",
  year =         "1994",
}

@Article{cnlp:vai95,
  author =       "M. Vai and Zhimin Xu",
  title =        "Representing knowledge by neural networks for
                 qualitative analysis and reasoning",
  journal =      "IEEE Transactions on Knowledge and Data Engineering",
  volume =       "7",
  number =       "5",
  pages =        "683--90",
  year =         "1995",
}

@InProceedings{cnlp:parfitt94,
  author =       "S. H. Parfitt",
  title =        "An {ANN} model of anaphora: implications for
                 nativism",
  booktitle =    "Proceedings of the International Conference on
                 Artificial Neural Networks --- ICANN94",
  address =      "Sorrento, Italy",
  publisher =    "Springer Verlag, Berlin, Germany",
  pages =        "222--225",
  volume =       "1",
  year =         "1994",
}

@InProceedings{cnlp:degerlache94,
  author =       "M. de Gerlache and A. Sperduti and A. Starita",
  title =        "Encoding conceptual graphs by labeling {RAAM}",
  booktitle =    "Proceedings of the International Conference on
                 Artificial Neural Networks --- ICANN94, Sorrento,
                 Italy, May 1994",
  publisher =    "Springer Verlag",
  address =      "Germany",
  pages =        "272--275",
  volume =       "1",
  year =         "1994",
}

@InProceedings{cnlp:sanfeliu95,
  author =       "A. San Feliu and R. Alquezar",
  title =        "Active grammatical inference: {A} new learning
                 methodology",
  booktitle =    "Shape, Structure and Pattern Recognition, Nahariya,
                 Israel, October 1994",
  publisher =    "World Scientific",
  address =      "Singapore",
  pages =        "191--200",
  year =         "1995",
}

@InProceedings{cnlp:sugiyama95,
  author =       "S. Sugiyama",
  title =        "A neural network application to semantic and logic
                 recognition",
  booktitle =    "IEEE International Conference on Systems, Man and
                 Cybernetics --- Intelligent Systems for the 21st
                 Century",
  address =      "Vancouver, BC, Canada",
  publisher =    "IEEE, New York, NY:USA",
  pages =        "3168--3173",
  volume =       "4",
  year =         "1995",
}

@InProceedings{cnlp:takane95,
  author =       "Y. Takane and Y. Oshima-Takane and T. R. Shultz",
  title =        "Network analyses: the case of first and second
                 pronouns",
  booktitle =    "IEEE International Conference on Systems, Man and
                 Cybernetics --- Intelligent Systems for the 21st
                 Century",
  address =      "Vancouver, BC, Canada",
  publisher =    "IEEE, New York, NY:USA",
  pages =        "3594--3599",
  volume =       "4",
  year =         "1995",
}

@InProceedings{cnlp:ries95,
  author =       "K. Ries and Finn Dag Buo and Ye Yi Wang",
  title =        "Improved Language Modelling by Unsupervised
                 Acquisition of Structure",
  booktitle =    "Proceedings of the 1995 International Conference on
                 Acoustics, Speech and Signal Processing",
  address =      "Detroit, MI, USA",
  publisher =    "IEEE, New York, NY, USA",
  pages =        "193--196",
  volume =       "1",
  year =         "1995",
}

@InProceedings{cnlp:martinez95,
  author =       "W. M. Martinez",
  title =        "A Natural Language Processor with Neural Networks",
  booktitle =    "IEEE International Conference on Systems, Man and
                 Cybernetics --- Intelligent Systems for the 21st
                 Century, Vancouver, Canada, October 1995",
  publisher =    "IEEE",
  address =      "New York, NY",
  pages =        "3156--3161",
  volume =       "4",
  year =         "1995",
}

@InProceedings{cnlp:ding,
  author =       "Liya Ding",
  title =        "Neural Prolog: the concepts, construction and
                 mechanism",
  booktitle =    "IEEE International Conference on Systems, Man and
                 Cybernetics --- Intelligent Systems for the 21st
                 Century, Vancouver, Canada, October 1995",
  publisher =    "IEEE",
  address =      "New York, NY",
  pages =        "3603--3608",
  volume =       "4",
  year =         "1995",
}

@InProceedings{cnlp:wu95,
  author =       "Xin Yu Wu and M. McTear and P. Ojha",
  title =        "A representational scheme for a hybrid natural
                 language processing system",
  booktitle =    "IEEE International Conference on Systems, Man and
                 Cybernetics --- Intelligent Systems for the 21st
                 Century",
  address =      "Vancouver, BC, Canada",
  publisher =    "IEEE, New York, NY:USA",
  pages =        "3162--3167",
  volume =       "4",
  year =         "1995",
}

@InProceedings{cnlp:kasabov95,
  author =       "N. K. Kasabov",
  title =        "Hybrid Connectionist Fuzzy Systems for Speech
                 Recognition and the use of Connectionist Production
                 Systems",
  booktitle =    "Advances in Fuzzy Logic, Neural Networks and Genetic
                 Algorithms, IEEE/Nugoya University World Wisepersons
                 Workshop, Nagoya, Japan, August 1994",
  publisher =    "Springer-Verlag 1995",
  pages =        "19--33",
  year =         "1995",
}

@InProceedings{cnlp:cooper94,
  author =       "T. Cooper",
  title =        "A language for connectionist pattern-recognition",
  booktitle =    "World Congress on neural networks-San Diego,
                 International Neural Network Society Annual Meeting,
                 San Deigo, CA, June 1994",
  volume =       "3",
  pages =        "156--161",
  publisher =    LEA,
  address =      LEAaddr,
  ISBN =         "0-80-581745-X",
  year =         "1994",
}

@InProceedings{cnlp:miikkulainen94a,
  author =       "R. Miikkulainen and D. Bijwaard",
  title =        "Parsing embedded clauses with distributed neural
                 networks",
  booktitle =    "Proceedings of the Twelfth National Conference on
                 Artificial Intelligence, Seattle, WA, July 1994",
  volume =       "1 and 2",
  pages =        "858--864",
  publisher =    "MIT Press",
  address =      "Cambridge MA",
  ISBN =         "0-26-261102-3",
  year =         "1994",
}

@InProceedings{cnlp:buo94,
  author =       "F. D. Buo and T. S. Polzin and A. Waibel",
  title =        "Learning complex output representations in
                 connectionist parsing of spoken language",
  booktitle =    "Proceedings of the International Conference on
                 Acoustics, Speech and Signal Processing, April 1994",
  volume =       "1",
  pages =        "365--368",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  ISBN =         "0-78-031775-0",
  year =         "1994",
}

@InProceedings{cnlp:daugherty94,
  author =       "K. G. Daugherty and M. Hare",
  title =        "The English past tense --- is this the extent of
                 language- learning by connectionist networks?",
  booktitle =    "1994 IEEE International Conference on Neural Networks,
                 Orlando, FL, Jun 1994",
  volume =       "1--7",
  year =         "1994",
  pages =        "2303--2308",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  ISBN =         "0-78-031901-X",
}

@InProceedings{cnlp:boden95,
  author =       "M. Bod\`{e}n and L. F. Niklasson",
  title =        "Features of distributed representations for
                 tree-structures --- {A} study of {RAAM}",
  booktitle =    "Current trends in Connectionism --- Proceedings of the
                 Swedish Conference on Connectionism, Skovde, Sweden,
                 March, 1995",
  year =         "1995",
  pages =        "121--139",
  editor =       "L. F. Niklasson and M. Bod\`{e}n",
  publisher =    LEA,
  address =      LEAaddr,
  ISBN =         "0-80-581997-5",
}

@InProceedings{cnlp:lyon95,
  author =       "C. Lyon and B. Dickerson",
  title =        "A fast partial parse of natural-language sentences
                 using a connectionist method",
  booktitle =    "Seventh Conference of the European Chapter of the
                 Association for Computational Linguistics, Dublin,
                 EIRE, March 1995",
  pages =        "215--222",
  publisher =    "Morgan Kaufmann",
  address =      "Palo Alto, CA",
  ISBN =         "1-55-860366-2",
  year =         "1995",
}

@Article{cnlp:sun95c,
  author =       "R. Sun",
  title =        "Structuring knowledge in vague domains",
  journal =      "IEEE Transactions on Knowledge and Data Engineering",
  year =         "1995",
  volume =       "7",
  number =       "1",
  pages =        "120--136",
  abstract =     "In this paper, we propose a model for structuring
                 knowledge in vague and continuous domains where
                 similarity plays a role in coming up with plausible
                 inferences. The model consists of two levels, one of
                 which is an inference network with nodes representing
                 concepts and links representing rules connecting
                 concepts, and the other is a microfeature-based replica
                 of the first level. Based on the interaction between
                 the concept nodes and microfeature nodes in the model,
                 inferences are facilitated and knowledge not explicitly
                 encoded in a system can be deduced via mixed similarity
                 matching and rule application. The model is able to
                 take account of many important desiderata of plausible
                 reasoning and produces sensible conclusions
                 accordingly. Examples will be presented to illustrate
                 the utility of the model in structuring knowledge to
                 enable useful inferences to be carried out in several
                 domains.",
}

@Article{cnlp:yeung95,
  author =       "D. S. Yeung and H. S. Fong",
  title =        "A knowledge matrix representation for a rule-mapped
                 neural- network",
  journal =      "Neurocomputing",
  year =         "1995",
  volume =       "7",
  number =       "2",
  pages =        "123--144",
  abstract =     "Neural networks are noted for their learning and
                 generalizing capabilities. However, their advancement
                 and applicabilities are severely limited by the low
                 comprehensibility of their internal knowledge.
                 Previously, the authors have proposed a rule-mapped
                 neural network model, by incorporating domain knowledge
                 initially. This paper suggests a tool named 'knowledge
                 matrix', that produces symbolic interpretations of the
                 network's response to an input. These interpretations
                 can be shown to enhance the reasoning power of the
                 system. Moreover, the system knowledge can be refined
                 explicably. The proposed approach is tested with a
                 Chinese character structure recognition problem. This
                 is an attempt to model a human learning process that is
                 commonly observed in many situations. For example, a
                 trainee may be given a set of provisional rules at the
                 very beginning which is expected to be moderated in
                 accordance with his forthcoming experience.",
}

@Article{cnlp:miikkulainen95,
  author =       "R. Miikkulainen",
  title =        "Script-based inference and memory retrieval in
                 subsymbolic story processing",
  journal =      "Applied Intelligence",
  year =         "1995",
  volume =       "5",
  number =       "2",
  pages =        "137--163",
  abstract =     "DISCERN is an integrated natural language processing
                 system built entirely from distributed neural networks.
                 It reads short narratives about stereotypical event
                 sequences, stores them in episodic memory, generates
                 fully expanded paraphrases of the narratives, and
                 answers questions about them. Processing in DISCERN is
                 based on hierarchically-organized backpropagation
                 modules, communicating through a central lexicon of
                 word representations. The lexicon is a double feature
                 map system that transforms each orthographic word
                 symbol into its semantic representation and vice versa.
                 The episodic memory is a hierarchy of feature maps,
                 where memories are stored ''one- shot'' at different
                 locations. Several high-level phenomena emerge
                 automatically from the special properties of
                 distributed neural networks in this model. DISCERN
                 learns to infer unmentioned events and unspecified role
                 fillers, generates expectations and defaults, and
                 exhibits plausible lexical access errors and memory
                 interference behavior. Word semantics, memory
                 organization, and appropriate script inferences are
                 automatically extracted from examples. DISCERN shows
                 that high- level natural language processing is
                 feasible through integrated subsymbolic systems.
                 Subsymbolic control of high- level behavior and
                 representing and learning abstractions are the two main
                 challenges in scaling up the approach to more
                 open-ended tasks.",
}

@Article{cnlp:barnden95,
  author =       "J. A. Barnden",
  title =        "High-level reasoning, computational challenges for
                 connectionism, and the conposit solution",
  journal =      "Applied Intelligence",
  year =         "1995",
  volume =       "5",
  number =       "2",
  pages =        "103--135",
  abstract =     "Sophisticated symbol processing in connectionist
                 systems can be supported by two primitive
                 representational techniques called Relative-Position
                 Encoding (RPE) and Pattern-Similarity Association
                 (PSA), and a selection technique called Temporal-
                 Winner-Take-All (TWTA). TWTA effects winner-take-all
                 selection on the basis of fine signal-timing
                 differences as opposed to activation-level differences.
                 Both RPE and PSA are far the encoding of highly
                 temporary associations between representations. RPE is
                 based on the way activation patterns are positioned
                 relative to each other within a network. Under PSA, two
                 patterns are temporarily associated if they have
                 (suitable) subpatterns that are (suitably) similar. The
                 article shows how particular versions of the primitives
                 are used to good effect in a system called
                 Conposit/SYLL. This is a connectionist implementation
                 of a slightly simplified version of a complex existing
                 psychological theory, namely Johnson- Laird's account
                 of syllogistic reasoning. The computational processes
                 in this theory present a major implementational
                 challenge to connectionism. The challenge lies in the
                 mutability, multiplicity, and diversity of the working
                 memory structures, and the elaborateness of the
                 processing needed for them. Conposit/SYLL's techniques
                 allow it to meet the challenge. The implementation of
                 symbolic processing in Conposit/SYLL is an interesting
                 application of connectionism partly because it
                 significantly affects the design of the symbolic
                 processing level itself. In particular, it encourages
                 the use of associative as opposed to pointer-based data
                 structures, and the use of random as opposed to ordered
                 iteration over sets of data structures. In addition,
                 the article discusses Conposit/SYLL's somewhat unusual
                 variable- binding approach.",
}

@Article{cnlp:tani95,
  author =       "J. Tani and N. Fukumura",
  title =        "Embedding a grammatical description in deterministic
                 chaos --- an experiment in recurrent neural learning",
  journal =      "Biological Cybernetics",
  year =         "1995",
  volume =       "72",
  number =       "4",
  pages =        "365--370",
  abstract =     "We consider the modeling process of a ''biological''
                 agent by combining the concepts of neuroinformatics and
                 deterministic chaos. We assume that an agent observes a
                 target process as a stochastic symbolic process, which
                 is restricted by grammatical constraints. Our main
                 hypothesis is that an agent would learn the target
                 model by reconstructing pan equivalent quasi-
                 stochastic process on its deterministic neural
                 dynamics. We employed a recurrent neural network (RNN),
                 which is regarded as an adjustable deterministic
                 dynamical system. Then, we conducted an experiment to
                 observe how the RNN learns to reconstruct the target
                 process, represented by a stochastic finite state
                 machine in the simulation. The result revealed the
                 capability of the RNN to evolve, by means of learning,
                 toward chaos, which is able to mimic a target's
                 stochastic process. We precisely analyzed the
                 evolutionary process as well as the internal
                 representation of the neural dynamics obtained. This
                 analysis enabled us to clarify an interesting mechanism
                 of the self-organization of chaos by means of neural
                 learning, and also showed how grammar can be embedded
                 in the evolved deterministic chaos.",
}

@Article{cnlp:robinson95,
  author =       "W. S. Robinson",
  title =        "Brain symbols and computationalist explanation",
  journal =      "Minds and machines",
  year =         "1995",
  volume =       "5",
  number =       "1",
  pages =        "25--44",
  abstract =     "Computationalist theories of mind require brain
                 symbols, that is, neural events that represent kinds or
                 instances of kinds. Standard models of computation
                 require multiple inscriptions of symbols with the same
                 representational content. The satisfaction of two
                 conditions makes it easy to see how this requirement is
                 met in computers, but we have no reason to think that
                 these conditions are satisfied in the brain. Thus, if
                 we wish to give computationalist explanations of human
                 cognition, without committing ourselves a priori to a
                 strong and unsupported claim in neuroscience, we must
                 first either explain how we can provide multiple brain
                 symbols with the same content, or explain how we can
                 abandon standard models of computation. It is argued
                 that both of these alternatives require us to explain
                 the execution of complex tasks that have a
                 cognition-like structure. Circularity or regress are
                 thus threatened, unless noncomputationalist principles
                 can provide the required explanations. But in the
                 latter case, we do not know that noncomputationalist
                 principles might not bear most of the weight of
                 explaining cognition. Four possible types of
                 computationalist theory are discussed; none appears to
                 provide a promising solution to the problem. Thus,
                 despite known difficulties in noncomputationalist
                 investigational we have every reason to pursue the
                 search for noncomputationalist principles in cognitive
                 theory.",
}

@Article{cnlp:lloyd95,
  author =       "D. Lloyd",
  title =        "Consciousness - {A} connectionist manifesto",
  journal =      "Minds and Machines",
  year =         "1995",
  volume =       "5",
  number =       "2",
  pages =        "161--185",
  abstract =     "Connectionism and phenomenology can mutually inform
                 and mutually constrain each other. In this manifesto I
                 outline an approach to consciousness based on
                 distinctions developed by connectionists. Two core
                 identities are central to a connectionist theory of
                 consciousness: conscious states of mind are identical
                 to occurrent activation patterns of processing units;
                 and the variable dispositional strengths on connections
                 between units store latent and unconscious information.
                 Within this broad framework, a connectionist model of
                 consciousness succeeds according to the degree of
                 correspondence between the content of human
                 consciousness (the world as it is experienced) and the
                 interpreted content of the network. Constitutive self-
                 awareness and reflective self-awareness can be captured
                 in a model through its ability to respond to
                 self-reflexive information, identify self-referential
                 categories, and process information in the absence of
                 simultaneous input. The qualitative ''feel'' of
                 sensation appears in a model as states of activation
                 that are not fully discriminated by later processing.
                 Connectionism also uniquely explains several specific
                 features of experience. The most important of these is
                 the superposition of information in consciousness - our
                 ability to perceive more than meets the eye, and to
                 apprehend complex categorical and temporal information
                 in a single highly- cognized glance. This superposition
                 in experience matches a superposition of
                 representational content in distributed
                 representations.",
}

@Article{cnlp:giles95,
  author =       "C. L. Giles and D. Chen and G. Z. Sun and H. H. Chen
                 and Y. C. Lee and M. W. Goudreau",
  title =        "Constructive learning of recurrent neural networks ---
                 Limitations of recurrent casade correlation and a
                 simple solution",
  journal =      "IEEE Transactions on Neural Networks",
  year =         "1995",
  volume =       "6",
  number =       "4",
  pages =        "829--836",
  abstract =     "It is often difficult to predict the optimal neural
                 network size for a particular application, Constructive
                 or destructive methods that add or subtract neurons,
                 layers, connections, etc, might offer a solution to
                 this problem, We prove that one method, recurrent
                 cascade correlation, due to its topology, has
                 fundamental limitations in representation and thus in
                 its learning capabilities, It cannot represent with
                 monotone (i.e., sigmoid) and hard-threshold activation
                 functions certain finite state automata, We give a
                 ''preliminary'' approach on how to get ground these
                 limitations by devising a simple constructive training
                 method that adds neurons during training while still
                 preserving the powerful fully-recurrent structure, We
                 illustrate this approach by simulations which learn
                 many examples of regular grammars that the recurrent
                 cascade correlation method is unable to learn.",
}

@Article{cnlp:hillis95,
  author =       "A. E. Hillis and A. Caramazza",
  title =        "Representation of grammatical categories of words in
                 the brain",
  journal =      "Journal of Cognitive Neuroscience",
  year =         "1995",
  volume =       "7",
  number =       "3",
  pages =        "396--407",
  abstract =     "We report the performance of a patient who, as a
                 consequence of left frontal and temporoparietal
                 strokes, makes far more errors on nouns than on verbs
                 in spoken output tasks, but makes far more errors on
                 verbs than on nouns in written input tasks. This double
                 dissociation within a single patient with respect to
                 grammatical category provides evidence for the
                 hypothesis that phonological and orthographic
                 representations of nouns and verbs are processed by
                 independent neural mechanisms. Furthermore, the
                 opposite dissociation in the verbal output modality, an
                 advantage for nouns over verbs in spoken tasks, by a
                 different patient using the same stimuli has also been
                 reported (Caramazza & Hillis, 1991). This double
                 dissociation across patients on the same task indicates
                 that results cannot be ascribed to ''greater
                 difficulty'' with one type of stimulus, and provides
                 further evidence for the view that grammatical category
                 information is an important organizational principle of
                 lexical knowledge in the brain.",
}

@Article{cnlp:vai95a,
  author =       "M. K. Vai and Z. M. Xu",
  title =        "Representing knowledge by neural networks for
                 qualitative- analysis and reasoning",
  journal =      "IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING",
  year =         "1995",
  volume =       "7",
  number =       "5",
  pages =        "683--690",
  abstract =     "A systematic approach has been developed to construct
                 neural networks for qualitative analysis and reasoning.
                 These neural networks are used as specialized parallel
                 distributed processors for solving constraint
                 satisfaction problems. A typical application of such a
                 neural network is to determine a reasonable change of a
                 system after one or more of its variables are changed.
                 A six-node neural network is developed to represent
                 fundamental qualitative relations. A larger neural
                 network can be constructed hierarchically for a system
                 to be modeled by using six-node neural networks as
                 building blocks. The complexity of the neural network
                 building process is thus kept manageable. An example of
                 developing a neural network reasoning model for a
                 transistor equivalent circuit is demonstrated. The use
                 of this neural network model in the equivalent circuit
                 parameter extraction process is also described.",
}

@Article{cnlp:pinkas95,
  author =       "G. Pinkas",
  title =        "Reasoning, nonmonotonicity and learning in
                 connectionist networks that capture propositional
                 knowledge",
  journal =      "Artificial Intelligence",
  year =         "1995",
  volume =       "77",
  number =       "2",
  pages =        "203--247",
  abstract =     "The paper presents a connectionist framework that is
                 capable of representing and learning propositional
                 knowledge. An extended version of propositional
                 calculus is developed and is demonstrated to be useful
                 for nonmonotonic reasoning, dealing with conflicting
                 beliefs and for coping with inconsistency generated by
                 unreliable knowledge sources, Formulas of the extended
                 calculus are proved to be equivalent in a very strong
                 sense to symmetric networks (like Hopfield networks and
                 Boltzmann machines), and efficient algorithms are given
                 for translating back and forth between the two forms of
                 knowledge representation, A fast learning procedure is
                 presented that allows symmetric networks to learn
                 representations of unknown logic formulas by looking at
                 examples, A connectionist inference engine is then
                 sketched whose knowledge is either compiled from a
                 symbolic representation or learned inductively from
                 training examples. Experiments with large scale
                 randomly generated formulas suggest that the parallel
                 local search that is executed by the networks is
                 extremely fast on average. Finally, it is shown that
                 the extended logic can be used as a high-level
                 specification language for connectionist networks, into
                 which several recent symbolic systems may be mapped,
                 The paper demonstrates how a rigorous bridge can be
                 constructed that ties together the (sometimes opposing)
                 connectionist and symbolic approaches.",
}

@Article{cnlp:park95,
  author =       "N. S. Park and D. Robertson and K. Stenning",
  title =        "Extension of the temporal synchrony approach to
                 dynamic variable binding in a connectionist inference
                 system",
  journal =      "Knowledge-Based Systems(UK)",
  year =         "1995",
  volume =       "8",
  number =       "6",
  pages =        "345--357",
  abstract =     "The relationship between symbolism and connectionism
                 has been one of the major issues in recent artificial
                 intelligence research. An increasing number of
                 researchers from each side have tried to adopt the
                 desirable characteristics of the approach. A major open
                 question in this field is the extent to which a
                 connectionist architecture can accommodate basic
                 concepts of symbolic inference, such as a dynamic
                 variable binding mechanism and a rule and fact encoding
                 mechanism involving nary predicates. One of the current
                 leaders in this area is the connectionist rule-based
                 system proposed by Shastri and Ajjanagadde. The paper
                 demonstrates that the mechanism for variable binding
                 which they advocate is fundamentally limited, and it
                 shows how a reinterpretation of the primitive
                 components and corresponding modifications of their
                 system can extend the range of inference which can be
                 supported. Our extension hinges on the basic structural
                 modification of the network components and further
                 modifications of the rule and fact encoding mechanism.
                 These modifications allow the extended model to have
                 more expressive power in dealing with symbolic
                 knowledge as in the unification of terms across many
                 groups of unifying arguments.",
}

@Article{cnlp:setiono95,
  author =       "R. Setiono and H. Liu",
  title =        "Symbolic representation of neural networks",
  journal =      "Computer",
  year =         "1996",
  volume =       "29",
  number =       "3",
  pages =        "71",
  abstract =     "The highly nonlinear nature of neural networks'
                 input-to-output mapping makes it difficult to describe
                 how they arrive at predictions. Thus, although their
                 predictive accuracy is satisfactory for applications
                 from finance to medicine, they have long been thought
                 of as ''black boxes.'' The authors propose to
                 understand a neural network via rules extracted from
                 it. Their algorithm, NeuroRule, extracts rules from a
                 standard feed-forward neural network, with network
                 training and pruning via the simple, widely used back-
                 propagation method. The extracted rules, a one-to-one
                 mapping of the pruned network, are compact and
                 comprehensible and do not involve weight values. The
                 authors' experiments show that neural-network-based
                 rules are as accurate and compact as
                 decision-tree-based rules, which are widely regarded as
                 explicit and understandable. Thus, using rules
                 extracted by NeuroRule, neural networks become
                 understandable and could lose their black-box
                 reputation.",
}

@Article{cnlp:itsuki95,
  author =       "N. Itsuki",
  title =        "Acquisition of internal representation by learning of
                 identity- mapping using overload learning",
  journal =      "Lecture Notes in Computer Science",
  year =         "1995",
  volume =       "930",
  pages =        "971--978",
  abstract =     "Acquisition of internal representation by neural
                 networks that learn identity-mappings is one of
                 important methods for feature-abstraction. It is,
                 however, difficult to decide the number of hidden units
                 of the networks. in this article, I show a way to apply
                 the overload learning (OLL) technique to overcome this
                 problem. Because OLL causes to reduce the number of
                 effective dimensions of hidden patterns, networks can
                 get reduced internal representation by learning.
                 Moreover, I show that this technique is useful to get
                 spatial representation from symbolic representation,
                 and to integrate various types of information.",
}

@Article{cnlp:santos95,
  author =       "J. Santos and R. P. Otero and J. Mira",
  title =        "{NETTOOL} --- {A} hybrid connectionist symbolic
                 development environment",
  journal =      "Lecture Notes in Computer Science",
  year =         "1995",
  volume =       "930",
  pages =        "658--665",
  abstract =     "In this work we present an environment, NEITOOL, for
                 the development of hybrid connectionist-symbolic
                 systems, in which the connectionist representation is
                 based on the same knowledge representation model as
                 that of symbolic systems. The hybridation between the
                 knowledge elements is local, the connectionist training
                 algorthm is also localized in each element of the
                 network so that the knowledge required for the learning
                 process is a part of it. There is also the possibility
                 of including capabilities for inferential level
                 processing in the elements of the network.",
}

@Article{cnlp:markov91,
  author =       "Z. Markov",
  title =        "A tool for building connectionist-like networks based
                 on term unification",
  journal =      "Lecture Notes in Artificial Intelligence",
  year =         "1991",
  vol =          "567",
  pages =        "199--213",
  abstract =     "The paper presents a network modeling tool called
                 Net-Clause Language (NCL), integrating some
                 connectionist-like and some symbolic processing
                 features in a unified computational environment. Unlike
                 the other connectionist symbol processing approaches,
                 NCL represents and processes symbols not by
                 implementing them as patterns of activity in a
                 traditional neural network, rather it uses some
                 connectionist ideas to organize symbolic computation in
                 a more flexible way. The paper presents two examples
                 (deductive inference and image processing) which show
                 how the connectionist-like and symbolic features of NCL
                 can benefit each from the other.",
}

@InProceedings{cnlp:kawada95,
  author =       "M. Kawada",
  title =        "A construction of neural-net based {AI} systems",
  booktitle =    "Proceedings of the 1st IEEE Conference on the
                 Engineering of Complex Systems, held jointly with 5th
                 CSESAW, 3rd IEEE RTAW and 20thIFAC/IFIP WRTP, Fort
                 Lauderdale, FL, November 1995",
  publisher =    "IEEE Computing Society Press",
  address =      "Los Alamitos, CA",
  pages =        "424--427",
  year =         "1995",
}

@InProceedings{cnlp:blanzieri95,
  author =       "E. Blanzieri and A. Giordina",
  title =        "Mapping Symbolic Knowledge into locally receptive
                 field networks",
  booktitle =    "Topics in Artificial Intelligence, 4th Congress of the
                 Italian Association for Artificial Intelligence, AI*IA
                 '95, Proceedings, Florence, Italy, October 1995",
  pages =        "267--278",
  year =         "1995",
}

@InProceedings{cnlp:khosla95,
  author =       "R. Khosla and T. Dillon",
  title =        "Knowledge Modeling in Integrated
                 Symbolic-Connectionist System",
  booktitle =    "IEEE International Conference on Systems, Man and
                 Cybernetics --- Intelligent Systems for the 21st
                 Century, Vancouver, Canada, October 1995",
  publisher =    "IEEE",
  addresss =     "New York, NY",
  pages =        "3879--3883",
  volume =       "5",
  year =         "1995",
}

@Article{cnlp:fu95,
  author =       "LiMin Fu",
  title =        "Introduction to Knowledge Based Neural Networks",
  journal =      "Knowledge Based Systems (UK)",
  volume =       "8",
  number =       "6",
  pages =        "299--300",
  year =         "1995",
}

@Article{cnlp:opitz95,
  author =       "D. W. Opitz and J. W. Shavlik",
  title =        "Dynamically adding symbolically meaningful nodes to
                 knowledge based neural networks",
  journal =      "Knowledge Based Systems (UK)",
  volume =       "8",
  number =       "6",
  pages =        "301--311",
  year =         "1995",
}

@Article{cnlp:andrews95,
  author =       "R. Andrews and J. Diederich and A. B. Tickle",
  title =        "Survey and critique of techniques for extracting rules
                 from trained artificical neural networks",
  journal =      "Knowledge Based Systems (UK)",
  volume =       "8",
  number =       "6",
  pages =        "378--389",
  year =         "1995",
}

@InProceedings{cnlp:lee95,
  author =       "Jong-Hyeok Lee and Geunbie Lee",
  title =        "A dependency parser of Korean based on
                 connectionist/symbolic techniques",
  booktitle =    "Progress in Artificial Intelligence, 7th Portugese
                 Conference on Artificial Intelligence, EIA'95,
                 Proceedings, Funchal, Portugal, October 1995",
  publisher =    "Springer-Verlag",
  address =      "Berlin, Germany",
  pages =        "465--471",
  year =         "1995",
}

@InProceedings{cnlp:michos95,
  author =       "S. E. Michos",
  title =        "A hybrid knowledge representation model in a natural
                 language interface to {MSDOS}",
  booktitle =    "Proceedings of the 7th Conference on Tools with
                 Artificial Intelligence, Herndon, VA, November 1995",
  publisher =    "IEEE Computing Society Press",
  address =      "Los Alamitos, CA",
  pages =        "480--483",
  year =         "1995",
}

@InProceedings{cnlp:kopecz95,
  author =       "K. Kopecz",
  title =        "Unsupervised learning if sequences on maps with
                 lateral connections",
  booktitle =    "International Conference on Artificial Neural Networks
                 ICANN '95",
  address =      "Paris, France",
  volume =       "1",
  pages =        "431--436",
  year =         "1995",
}

@InProceedings{cnlp:misic-flogel95,
  author =       "J. Misic-Flogel",
  title =        "Temporal sequence processing: learning recognition and
                 tracking using neural nets",
  booktitle =    "International Conference on Artificial Neural Networks
                 ICANN '95",
  address =      "Paris, France",
  volume =       "1",
  pages =        "155--160",
  year =         "1995",
}

@InProceedings{cnlp:blanchet95,
  author =       "P. Blanchet and F. Alexandre",
  title =        "A hierarchical connectionist architecture for learning
                 internal representations of behaviours",
  booktitle =    "International Conference on Artificial Neural Networks
                 ICANN '95",
  address =      "Paris, France",
  volume =       "2",
  pages =        "183--188",
  year =         "1995",
}

@InProceedings{cnlp:callan95,
  author =       "R. E. Callan and D. Palmer-Brown",
  title =        "Analytical Derivation of a {(S)RAAM}",
  booktitle =    "International Conference on Artificial Neural Networks
                 ICANN '95",
  address =      "Paris, France",
  volume =       "2",
  pages =        "485--490",
  year =         "1995",
}

@Article{cnlp:vandervelde95,
  author =       "F. van der Velde",
  title =        "Symbol Manipulation with neural networks: production
                 of a context free language using a modifiable working
                 memory",
  journal =      "Connection Science",
  volume =       "7",
  number =       "3--4",
  pages =        "247--280",
  year =         "1995",
}

@Article{cnlp:lin95,
  author =       "K. H. C. Lin and Tung-bo Chen and Von Wun Soo",
  title =        "Neural network learning and encoding of thematic role
                 assignments in parsing of simple Chinese sentences",
  journal =      "Journal of Information Sciences and Engineering",
  volume =       "11",
  number =       "1",
  pages =        "109--126",
  year =         "1995",
}

@InProceedings{cnlp:lyon95a,
  author =       "C. Lyon and R. Frank",
  title =        "Neural network design for a natural language parser",
  booktitle =    "International Conference on Artificial Neural Networks
                 ICANN '95",
  address =      "Paris, France",
  volume =       "1",
  pages =        "105--110",
  year =         "1995",
}

@TechReport{cnlp:ho95a,
  author =       "Edward Kei Shiu Ho and Lai Wan Chan",
  title =        "Confluent Preorder Parsing",
  institution =  "Department of Computer Science and Engineering, The
                 Chinese University of Hong Kong",
  address =      "Shatin, N.T., Hong Kong",
  number =       "CS-TR-95-03",
  year =         "1995",
}

@TechReport{cnlp:ho95b,
  author =       "Edward Kei Shiu Ho and Lai Wan Chan",
  title =        "Efficient Connectionist Representations of Syntactic
                 Parse Trees for Grammatical Inference",
  institution =  "Department of Computer Science and Engineering, The
                 Chinese University of Hong Kong",
  address =      "Shatin, N.T., Hong Kong",
  number =       "CS-TR-95-15",
  year =         "1995",
}

@InProceedings{cnlp:ho95c,
  author =       "Edward Kei Shui Ho and Lai Wan Chan",
  title =        "Syntactic Parsing Using {RAAM}",
  booktitle =    "Proceedings of World Congress on Neural Networks,
                 Washington DC, July 1995",
  publisher =    LEA,
  address =      LEAaddr,
  volume =       "1",
  pages =        "485--488",
  year =         "1995",
}

@InProceedings{cnlp:ho95d,
  author =       "Edward Kei Shiu Ho and Lai Wan Chan",
  title =        "Linearization + Confluent Inference = Efficient
                 Connectionist Representations of Parse Trees for
                 Grammatical Inference",
  booktitle =    "Proceedings of International Symposium on Artificial
                 Neural Networks, Hsinchu, Taiwan, Dec 18--20 1995",
  pages =        "IS--24--IS--31",
  year =         "1995",
}

@InProceedings{cnlp:ho96,
  author =       "Edward Kei Shiu Ho and Lai Wan Chan",
  title =        "Confluent preorder parser as a finite state automata",
  booktitle =    "Proceedings of International Conference on Artificial
                 Neural Networks ICANN'96, Bocham, Germany, July 16--19
                 1996",
  publisher =    "Springer-Verlag",
  address =      "Berlin",
  pages =        "899--904",
  year =         "1996",
}

@TechReport{cnlp:blank91,
  author =       "D. S. Blank and L. A. Meeden and J. B. Marshall",
  title =        "Exploring the Symbolic/Subsymbolic Continuum: {A} Case
                 Study of {RAAM}",
  institution =  "Department of Computer Science, Indiana University",
  address =      "Bloomington, Indiana",
  number =       "TR 332",
  year =         "1991",
}

@InCollection{cnlp:blank92,
  author =       "D. S. Blank and L. A. Meeden and J. B. Marshall",
  title =        "Exploring the Symbolic/Subsymbolic Continuum: {A} Case
                 Study of {RAAM}",
  editor =       "J Dinsmore",
  booktitle =    "Symbolic and Connectionist Paradigms: Closing the
                 Gap",
  publisher =    LEA,
  address =      LEAaddr,
  pages =        "113--148",
  year =         "1992",
}

@InProceedings{cnlp:hadley92,
  author =       "R. F. Hadley",
  title =        "Compositionality and Systematicity in Connectionist
                 Language Learning",
  booktitle =    "Proceedings of the Fourteenth Annual Conference of the
                 Cognitive Science Society, Bloomington, IN, Jul 1992",
  publisher =    LEA,
  address =      LEAaddr,
  pages =        "659--664",
  year =         "1992",
}

@InProceedings{cnlp:weber92,
  author =       "V. Weber",
  title =        "Connectionist Unification with a distributed
                 representation",
  booktitle =    "Proceedings of the International Joint Conference on
                 Neural Networks -- IJCNN '92, Beijing, China",
  publisher =    "IEEE",
  address =      "Piscataway, NJ",
  pages =        "555--560",
  year =         "1992",
}

@InCollection{cnlp:weber93a,
  author =       "V. Weber",
  title =        "Connectionist Unifying Prolog",
  booktitle =    "Artificial Neural Networks and Genetic Algorithms",
  editor =       "Albrecht R. F. and Reeves C. R. and Steele N. C.",
  edition =      "2nd",
  publisher =    "Springer Verlag",
  pages =        "213--220",
  year =         "1993",
}

@InProceedings{cnlp:weber93b,
  author =       "V. Weber",
  title =        "Unification in Prolog by connectionist models",
  booktitle =    "Proceedings of the Fourth Australian Conference on
                 Neural Networks, ACNN'93 (Melbourne, Australia)",
  volume =       "A",
  pages =        "A:5--8",
  publisher =    "SEDAL",
  address =      "Sydney",
  year =         "1993",
}

@InProceedings{cnlp:french94,
  author =       "R. M. French",
  title =        "Dynamically constraining connectionist networks to
                 produce distributed, orthogonal representations to
                 reduce catastrophic interference",
  booktitle =    "Proceedings of the Sixteenth Annual Conference of the
                 Cognitive Science Society, Atlanta, GA, August 1994",
  publisher =    LEA,
  address =      LEAaddr,
  editors =      "Ram A. and Eiselt K.",
  pages =        "335--340",
  year =         "1994",
}

@Book{cnlp:russell95,
  author =       "S. J. Russell and P. Norvig",
  title =        "Artificial Intelligence: {A} Modern Approach",
  publisher =    "Prentice Hall International",
  address =      "New Jersey, USA",
  year =         "1995",
}

@Article{cnlp:hammerton98,
  author =       "J. A. Hammerton",
  title =        "Holistic Computation: Reconstructing a muddled
                 concept",
  journal =      "Connection Science",
  volume =       "10",
  number =       "1",
  pages =        "3--19",
  year =         "1998",
}

@InCollection{cnlp:niklasson97,
  author =       "L. Niklasson and N. E. Sharkey",
  title =        "Systematicity and generalization in compositional
                 connectionist representations",
  editor =       "G. Dorffner",
  booktitle =    "Neural Networks and a New Artificial Intelligence",
  publisher =    "Thomson Computer Press",
  address =      "London",
  pages =        "217--232",
  year =         "1997",
}

@Article{cnlp:narayanan96,
  author =       "S. Narayanan and G. E. Taglianni and E. W. Page",
  title =        "Enhancing {MLP} networks using a distributed
                 representation",
  journal =      "IEEE Transactions on Systems, Man and Cybernetics B",
  volume =       "26",
  number =       "1",
  pages =        "143--149",
  year =         "1996",
}

@Book{cnlp:levy95,
  editor =       "J. Levy and D. Baraktairis and J. Bullinaria and P.
                 Cairn",
  title =        "Connectionist Models of Memory and Language",
  publisher =    "UCL Press",
  address =      "London",
  year =         "1995",
  ISBN =         "1 85728 368 6",
}

@Article{cnlp:callan97,
  author =       "R. E. Callan and D. Palmer-Brown",
  title =        "{(S)RAAM}: An Analytical Technique for Fast and
                 Reliable Derivation of Connectionist Symbol Structure
                 Representations",
  journal =      "Connection Science",
  volume =       "9",
  number =       "2",
  pages =        "139--159",
  year =         "1997",
}

@TechReport{cnlp:sperduti93,
  author =       "A. Sperduti",
  title =        "{L}abeling {RAAM}",
  institution =  "International Computer Science Institute",
  address =      "Berkeley, California",
  number =       "TR-93-029",
  year =         "1993",
}

@InProceedings{cnlp:sperduti95,
  author =       "A. Sperduti and A. Starita and Goller C.",
  title =        "Learning Distributed Representations for the
                 Classfication of Terms",
  booktitle =    "Proceedings of the 14th International Joint Conference
                 on Artificial Intelligence (IJCAI-95), Montreal,
                 Canada, 20 -- 25 August 1995",
  editor =       "C. S. Mellish",
  pages =        "509--515",
  year =         "1995",
}

@Article{cnlp:garcia-carpintero95,
  author =       "M. Garcia-Carpintero",
  title =        "The philosophical import of connectionism, a critical
                 notice of Andy Clark's Associative Engines",
  journal =      "Mind and Language",
  volume =       "10",
  number =       "4",
  pages =        "370--401",
  year =         "1995",
}

@Article{cnlp:avner96,
  author =       "S. Avner",
  title =        "Extraction of comprehensive symbolic rules from a
                 multi-layer perceptron",
  journal =      "Engineering Applications of Artificial Intelligence",
  volume =       "9",
  number =       "2",
  pages =        "137--143",
  year =         "1996",
}

@InProceedings{cnlp:chan95,
  author =       "S. W. K. Chan and J. Franklin",
  title =        "A neurosymbolic integrated model for semantic
                 ambiguation resolution",
  booktitle =    "Proceedings of the IEEE ICNN 1995",
  volume =       "6",
  pages =        "2965--70",
  year =         "1995",
  note =         "GET FULL DETAILS from July 1996 Inspecs",
}

@Book{cnlp:press92,
  author =       "W. H. Press and S. A. Teukolsky and W. T. Vetterling
                 and B. P. Flannery",
  title =        "Numerical Recipes in {C}: The Art of Scientific
                 Computing",
  edition =      "2nd",
  publisher =    "Cambridge University Press",
  year =         "1992",
}

@TechReport{cnlp:stolcke92,
  author =       "A Stolcke and D. Wu",
  title =        "Tree matching with recursive distributed
                 representations",
  number =       "TR-92-025",
  institution =  "International Computer Science Institute",
  address =      "Berkeley, California",
  year =         "1992",
}

@Article{cnlp:kohonen90,
  author =       "T. Kohonen",
  title =        "The Self-Organising Map",
  journal =      "Proceedings of the IEEE",
  volume =       "78",
  number =       "9",
  pages =        "1464--1480",
  year =         "1990",
}
