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Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing

  • Conference proceedings
  • © 1996

Overview

Editors:
  1. Stefan Wermter
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  2. Ellen Riloff
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  3. Gabriele Scheler
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 1040)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: IJCAI 1995.

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Table of contents (33 papers)

  1. Front Matter

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  2. Learning approaches for natural language processing

    • Stefan Wermter, Ellen Riloff, Gabriele Scheler
    Pages 1-16

  3. Separating learning and representation

    • Noel E. Sharkey, Amanda J. C. Sharkey
    Pages 17-32

  4. Natural language grammatical inference: A comparison of recurrent neural networks and machine learning methods

    • Steve Lawrence, Sandiway Fong, C. Lee Giles
    Pages 33-47

  5. Extracting rules for grammar recognition from Cascade-2 networks

    • Ross Hayward, Alan Tickle, Joachim Diederich
    Pages 48-60

  6. Generating English plural determiners from semantic representations: A neural network learning approach

    • Gabriele Scheler
    Pages 61-74

  7. Knowledge acquisition in concept and document spaces by using self-organizing neural networks

    • Werner Winiwarter, Erich Schweighofer, Dieter Merkl
    Pages 75-86

  8. Using hybrid connectionist learning for speech/language analysis

    • Volker Weber, Stefan Wermter
    Pages 87-101

  9. SKOPE: A connectionist/symbolic architecture of spoken Korean processing

    • Geunbae Lee, Jong -Hyeok Lee
    Pages 102-116

  10. Integrating different learning approaches into a multilingual spoken language translation system

    • P. Geutner, B. Suhm, F. -D. Buø, T. Kemp, L. Mayfield, A. E. McNair et al.
    Pages 117-131

  11. Learning language using genetic algorithms

    • Tony C. Smith, Ian H. Witten
    Pages 132-145

  12. A statistical syntactic disambiguation program and what it learns

    • Murat Ersan, Eugene Charniak
    Pages 146-159

  13. Training stochastic grammars on semantical categories

    • Wide R. Hogenhout, Yuji Matsumoto
    Pages 160-172

  14. Learning restricted probabilistic link grammars

    • Eva Wai-man Fong, Dekai Wu
    Pages 173-187

  15. Learning PP attachment from corpus statistics

    • Alexander Franz
    Pages 188-202

  16. A minimum description length approach to grammar inference

    • Peter Grünwald
    Pages 203-216

  17. Automatic classification of dialog acts with Semantic Classification Trees and Polygrams

    • Marion Mast, Heinrich Niemann, Elmar Nöth, Ernst Günter Schukat-Talamazzini
    Pages 217-229

  18. Sample selection in natural language learning

    • Sean P. Engelson, Ido Dagan
    Pages 230-245

  19. Learning information extraction patterns from examples

    • Scott B. Huffman
    Pages 246-260

  20. Implications of an automatic lexical acquisition system

    • Peter M. Hastings
    Pages 261-274
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Other volumes

  1. Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing

  2. Adaption and Learning in Multi-Agent Systems

Keywords

About this book

This book is based on the workshop on New Approaches to Learning for Natural Language Processing, held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI'95, in Montreal, Canada in August 1995.
Most of the 32 papers included in the book are revised selected workshop presentations; some papers were individually solicited from members of the workshop program committee to give the book an overall completeness. Also included, and written with the novice reader in mind, is a comprehensive introductory survey by the volume editors. The volume presents the state of the art in the most promising current approaches to learning for NLP and is thus compulsory reading for researchers in the field or for anyone applying the new techniques to challenging real-world NLP problems.

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