Subsymbolic Language Processing: PSYC Book Review

CALL FOR BOOK REVIEWERS Below is the Abstract of a Precis of SUBSYMBOLIC NATURAL LANGUAGE PROCESSING by Risto Mikkulainen (Precis retrievable from ftp://princeton.edu/pub/harnad/Psycoloquy filename: psyc.94.5.46.language-network.1.miikkulainen) This book has been selected for multiple review in PSYCOLOQUY. If you wish to submit a formal book review (see Instructions following Precis) please write to psyc(a)pucc.bitnet indicating what expertise you would bring to bear on reviewing the book if you were selected to review it. (If you have never reviewed for PSYCOLOQUY or Behavioral & Brain Sciences before, it would be helpful if you could also append a copy of your CV to your message.) If you are selected as one of the reviewers, you will be sent a copy of the book directly by the publisher (please let us know if you have a copy already). Reviews may also be submitted without invitation, but all reviews will be refereed. The author will reply to all accepted reviews. ---------------------------------------------------------------------- psycoloquy.94.5.46.language-network.1.miikkulainen Monday 8 Aug 1994 ISSN 1055-0143 (34 paragraphs, 1 fig, 1 note, 16 references, 609 lines) PSYCOLOQUY is sponsored by the American Psychological Association (APA) Copyright 1994 Risto Miikkulainen Precis of: SUBSYMBOLIC NATURAL LANGUAGE PROCESSING: AN INTEGRATED MODEL OF SCRIPTS, LEXICON, AND MEMORY Cambridge, MA: MIT Press, 1993 15 chapters, 403 Pages Risto Miikkulainen Department of Computer Sciences The University of Texas at Austin Austin, TX 78712 risto(a)cs.utexas.edu ABSTRACT: Distributed neural networks have been very successful in modeling isolated cognitive phenomena, but complex high-level behavior has been amenable only to symbolic artificial intelligence techniques. Aiming to bridge this gap, this book describes DISCERN, a complete natural language processing system implemented entirely at the subsymbolic level. In DISCERN, distributed neural network models of parsing, generating, reasoning, lexical processing and episodic memory are integrated into a single system that learns to read, paraphrase, and answer questions about stereotypical narratives. Using DISCERN as an example, a general approach to building high-level cognitive models from distributed neural networks is introduced, and the special properties of such networks are shown to provide insight into human performance. In this approach, connectionist networks are not only plausible models of isolated cognitive phenomena, but also sufficient constituents for generating complex, high-level behavior. KEYWORDS: computational modeling, connectionism, distributed neural networks, episodic memory, lexicon, natural language processing, scripts. XII. TABLE OF CONTENTS PART I Overview 1 Introduction 2 Background 3 Overview of DISCERN PART II Processing Mechanisms 4 Backpropagation Networks 5 Developing Representations in FGREP Modules 6 Building from FGREP Modules PART III Memory Mechanisms 7 Self-Organizing Feature Maps 8 Episodic Memory Organization: Hierarchical Feature Maps 9 Episodic Memory Storage and Retrieval: Trace Feature Maps 10 Lexicon PART IV Evaluation 11 Behavior of the Complete Model 12 Discussion 13 Comparison to Related Work 14 Extensions and Future Work 15 Conclusions APPENDICES A Story Data B Implementation Details C Instructions for Obtaining the DISCERN Software -------------------------------------------------------------------- PSYCOLOQUY Book Review Instructions The PSYCOLOQUY book review procedure is very similar to the commentary procedure except that it is the book itself, not a target article, that is under review. 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