Strategies for Natural Language Processing

By Wendy G. Lehnert; Martin H. Ringle | Go to book overview

6
A Framework for Conceptual Analyzers

Anatole V. Gershman Schlumberger-Doll Research Center


INTRODUCTION

Natural-language communication with computers is one of the most practical and at the same time one of the most difficult problems of computer science. The first thing that we have to do in such a communication is to look at the natural-language input and decide what this text means. Natural-language understanding involves two components: knowledge about concepts being communicated and knowledge about the ways these concepts are encoded in language. Without these two components, understanding is impossible. A person who has never heard of American football will not be able to understand a radio report on a football game even if it is given in that person's native language. Conversely, no one can understand a sentence in an unfamiliar language. What kinds of knowledge do we need to understand a natural-language text, how much knowledge, and how to apply it? These are the central problems of natural-language processing.

There are many ways of attacking the problem of natural-language understanding. At one end of the spectrum are analyzers that read the input sentences from left to right, very closely following every twist in syntax, trying to interpret every bit of information contained in the sentence. In most cases, these analyzers separate the syntactic and semantic parts of the analysis into separate consecutive stages, paying much more attention to the syntactic part at the expense of semantics ( Ginsparg, 1978; Marcus, 1977).

At the other end of the spectrum are the analyzers that skim through the text looking for certain types of information and paying attention only to the words and expressions relevant to the task ( DeJong, 1979, Schank, Lebovitz, &

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Strategies for Natural Language Processing
Table of contents

Table of contents

  • Title Page iii
  • Contents vii
  • Preface xiii
  • Abstracts xvii
  • I - Introduction 1
  • 1 - The State of the Art In Natural-Language Understanding 3
  • Acknowledgments 30
  • References 30
  • II - Implementation Issues 33
  • 2 - Realistic Language Comprehension 37
  • References 53
  • 3 - Natural Communication Between Person and Computer 55
  • Acknowledgments 86
  • References 86
  • 4 - Parsing and Comprehending With Word Experts (a Theory And Its Realization) 89
  • Acknowledgments 147
  • References 147
  • 5 - An Overview of the Frump System 149
  • Acknowledgments 175
  • References 175
  • 6 - A Framework for Conceptual Analyzers 177
  • References 196
  • III - Conversation And Discourse 199
  • 7 - Conversation Failure 203
  • References 220
  • 8 - Towards an Understanding Of Coherence in Discourse 223
  • Acknowledgments 242
  • References 242
  • 9 - Beyond Question Answering 245
  • Acknowledgments 271
  • References 271
  • 10 - Adversary Arguments and The Logic of Personal Attacks 275
  • Acknowledgments 293
  • References 294
  • IV - Knowledge Representation 295
  • 11 - Inference and Learning In Computer Model of The Development of Language Comprehension in a Young Child 299
  • Acknowledgments 325
  • References 325
  • 12 - Inferring Building Blocks For Knowledge Representation 327
  • Acknowledgments 343
  • References 343
  • 13 - Points: A Theory of the Structure Of Stories in Memory 345
  • References 373
  • 14 - Plot Units: a Narrative Summarization Strategy 375
  • Acknowledgments 411
  • References 411
  • V - Theoretical Issues 413
  • 15 - Metaphor: an Inescapable Phenomenon In Natural-Language Comprehension 415
  • Acknowledgments 432
  • References 433
  • 16 - Context Recognition In Language Cornprehension 435
  • Acknowledgments 453
  • References 453
  • 17 - Reminding and Memory Organization: an Introduction To Mops 455
  • Acknowledgments 493
  • References 493
  • 18 - Some Thoughts on Procedural Semantics 495
  • Acknowledgments 515
  • Notes on Contributors 517
  • Subject Index 523
  • Index 529
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