Artificial Neural Networks and Natural Language Processing

This entry gives an overview of work to date on natural language processing (NLP) using artificial neural networks (ANN). It is in three main parts: the first gives a brief introduction to ANNs, the second outlines some of the main issues in ANN-based NLP, and the third surveys specific application areas. Each part cites a representative selection of research literature that itself contains pointers to further reading

[1]  Bart Selman Connectionist systems for natural language understanding , 2004, Artificial Intelligence Review.

[2]  Werner Winiwarter,et al.  Knowledge acquisition in concept and document spaces by using self-organizing neural networks , 1995, Learning for Natural Language Processing.

[3]  A Prince,et al.  Optimality: From Neural Networks to Universal Grammar , 1997, Science.

[4]  S. Pinker,et al.  On language and connectionism: Analysis of a parallel distributed processing model of language acquisition , 1988, Cognition.

[5]  J. Fodor Connectionism and the problem of systematicity (continued): why Smolensky's solution still doesn't work , 1997, Cognition.

[6]  Alexander Shustorovich,et al.  Neural network positioning and classification of handwritten characters , 1996, Neural Networks.

[7]  Padhraic Smyth,et al.  Discrete recurrent neural networks for grammatical inference , 1994, IEEE Trans. Neural Networks.

[8]  Jerome A. Feldman,et al.  Structured connectionist models and language learning , 1993, Artificial Intelligence Review.

[9]  James L. McClelland,et al.  An interactive activation model of context effects in letter perception: Part 2. The contextual enhancement effect and some tests and extensions of the model. , 1982, Psychological review.

[10]  Jerome A. Feldman,et al.  Connectionist Models and Their Properties , 1982, Cogn. Sci..

[11]  Dominic Palmer-Brown,et al.  (S)RAAM: An Analytical Technique for Fast and Reliable Derivation of Connectionist Symbol Structure Representations , 1997, Connect. Sci..

[12]  Michael Gasser,et al.  Transfer in a Connectionist Model of the Acquisition of Morphology , 1995, ArXiv.

[13]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[14]  Mark S. Seidenberg,et al.  Language Acquisition and Use: Learning and Applying Probabilistic Constraints , 1997, Science.

[15]  Alex Waibel,et al.  Parsing with Connectionist Networks , 1991 .

[16]  V. Marchman,et al.  From rote learning to system building: acquiring verb morphology in children and connectionist nets , 1993, Cognition.

[17]  Garrison W. Cottrell,et al.  Time-delay neural networks: representation and induction of finite-state machines , 1997, IEEE Trans. Neural Networks.

[18]  Tim van Gelder,et al.  Compositionality: A Connectionist Variation on a Classical Theme , 1990, Cogn. Sci..

[19]  Samuel Kaski,et al.  Self organization of a massive document collection , 2000, IEEE Trans. Neural Networks Learn. Syst..

[20]  Stefan Wermter,et al.  SCREEN: learning a flat syntactic and semantic spoken language analysis using artificial neural networks , 1997 .

[21]  Dieter Merkl,et al.  Text classification with self-organizing maps: Some lessons learned , 1998, Neurocomputing.

[22]  C. Lee Giles,et al.  Learning a class of large finite state machines with a recurrent neural network , 1995, Neural Networks.

[23]  Noel E. Sharkey,et al.  Separating learning and representation , 1995, Learning for Natural Language Processing.

[24]  Michael G. Dyer,et al.  Perceptually Grounded Language Learning: Part 1—A Neural Network Architecture for Robust Sequence Association , 1993 .

[25]  Paul Smolensky,et al.  Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems , 1990, Artif. Intell..

[26]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[27]  Robert F. Hadley Systematicity in Connectionist Language Learning , 1994 .

[28]  T. Gelder,et al.  On Being Systematically Connectionist , 1994 .

[29]  James Henderson,et al.  A Connectionist Architecture for Learning to Parse , 1998, ACL.

[30]  Nick Chater,et al.  Connectionist natural language processing: the state of the art , 1999 .

[31]  B. MacWhinney,et al.  Implementations are not conceptualizations: Revising the verb learning model , 1991, Cognition.

[32]  G. Dell,et al.  Language production and serial order: a functional analysis and a model. , 1997, Psychological review.

[33]  Timo Honkela,et al.  WEBSOM - Self-organizing maps of document collections , 1998, Neurocomputing.

[34]  James L. McClelland,et al.  Graded state machines: The representation of temporal contingencies in simple recurrent networks , 1991, Machine Learning.

[35]  Michael J. Bennett,et al.  The Historical Background , 1997 .

[36]  Sandiway Fong,et al.  Natural language grammatical inference: a comparison of recurrent neural networks and machine learning methods , 1995, Learning for Natural Language Processing.

[37]  Tony A. Plate,et al.  Holographic reduced representations , 1995, IEEE Trans. Neural Networks.

[38]  P. Frasconi,et al.  Representation of Finite State Automata in Recurrent Radial Basis Function Networks , 1996, Machine Learning.

[39]  Jordan B. Pollack,et al.  Recursive Distributed Representations , 1990, Artif. Intell..

[40]  G. Marcus The acquisition of the English past tense in children and multilayered connectionist networks , 1995, Cognition.

[41]  Stefan Wermter,et al.  Neural Network Agents for Learning Semantic Text Classification , 2000, Information Retrieval.

[42]  Igor Aleksander,et al.  Successful naïve representation grounding , 2004, Artificial Intelligence Review.

[43]  James L. McClelland,et al.  An interactive activation model of context effects in letter perception: I. An account of basic findings. , 1981 .

[44]  A. Konig Interactive visualization and analysis of hierarchical neural projections for data mining , 2000 .

[45]  J. Elman,et al.  Learning and morphological change , 1995, Cognition.

[46]  Noel E. Sharkey,et al.  Connectionist representation techniques , 1991, Artificial Intelligence Review.

[47]  V. Marchman,et al.  U-shaped learning and frequency effects in a multi-layered perception: Implications for child language acquisition , 1991, Cognition.

[48]  Michael K. Tanenhaus,et al.  Parsing in a Dynamical System: An Attractor-based Account of the Interaction of Lexical and Structural Constraints in Sentence Processing , 1997 .

[49]  Jung-Hsien Chiang,et al.  A hybrid neural network model in handwritten word recognition , 1998, Neural Networks.

[50]  Noel E. Sharkey,et al.  Grounding computational engines , 1996, Artificial Intelligence Review.

[51]  P. Smolensky On the proper treatment of connectionism , 1988, Behavioral and Brain Sciences.

[52]  T. Horgan,et al.  Connectionism and the Philosophy of Mind , 1991 .

[53]  Peter Tiňo,et al.  Finite State Machines and Recurrent Neural Networks -- Automata and Dynamical Systems Approaches , 1995 .

[54]  J. Fodor,et al.  Connectionism and cognitive architecture: A critical analysis , 1988, Cognition.

[55]  Stefan Wermter,et al.  A Novel Modular Neural Architecture for Rule-Based and Similarity-Based Reasoning , 1998, Hybrid Neural Systems.

[56]  James L. McClelland,et al.  Learning and Applying Contextual Constraints in Sentence Comprehension , 1990, Artif. Intell..

[57]  C. Lee Giles,et al.  Stable Encoding of Large Finite-State Automata in Recurrent Neural Networks with Sigmoid Discriminants , 1996, Neural Computation.

[58]  Stefan Wermter,et al.  A Hybrid Symbolic/Connectionist Model for Noun Phrase Understanding , 1989 .

[59]  Raymond L. Watrous,et al.  Induction of Finite-State Languages Using Second-Order Recurrent Networks , 1992, Neural Computation.

[60]  Ronan G. Reilly A connectionist model of some aspects of anaphor resolution , 1984 .

[61]  Risto Miikkulainen,et al.  Integrated connectionist models: building AI systems on subsymbolic foundations , 1994, Proceedings Sixth International Conference on Tools with Artificial Intelligence. TAI 94.

[62]  Garrison W. Cottrell,et al.  Acquiring the Mapping from Meaning to Sounds , 1994, Connect. Sci..

[63]  J. Elman Learning and development in neural networks: the importance of starting small , 1993, Cognition.

[64]  Gabriele Scheler,et al.  Generating English plural determiners from semantic representations: a neural network learning approach , 1995, Learning for Natural Language Processing.

[65]  Michael G. Dyer,et al.  Perceptually Grounded Language Learning: Part 2 - DETE: A Neural/Procedural Model , 1994, Connect. Sci..

[66]  Sankar K. Pal,et al.  A connectionist system for learning and recognition of structures: Application to handwritten characters , 1995, Neural Networks.

[67]  Giovanni Soda,et al.  Recurrent neural networks and prior knowledge for sequence processing: a constrained nondeterministic approach , 1995, Knowl. Based Syst..

[68]  Allen Newell,et al.  Physical Symbol Systems , 1980, Cogn. Sci..

[69]  Rohini K. Srihari,et al.  Computational models for integrating linguistic and visual information: A survey , 2004, Artificial Intelligence Review.

[70]  Martin J. Adamson,et al.  B-RAAM: A Connectionist Model which Develops Holistic Internal Representations of Symbolic Structures , 1999, Connect. Sci..

[71]  Jordan B. Pollack,et al.  No harm intended: Marvin L. Minsky and Seymour A. Papert. Perceptrons: An Introduction to Computational Geometry, Expanded Edition. Cambridge, MA: MIT Press, 1988. Pp. 292. $12.50 (paper) , 1989 .

[72]  Patrick Juola,et al.  A connectionist model of English past tense and plural morphology , 1999 .

[73]  Andreas Stolcke,et al.  L0-The first five years of an automated language acquisition project , 1996, Artificial Intelligence Review.