Strategies for Natural Language Processing

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

36. THE CAR WAS FIXED BY MARY.

we will have the following memory configuration:

37.CONCEPT =CN1 = (REPAIR ACTOR nil OBJECT nil)
BUFFER =(CN2 = (CAR)
CN3 = (PERSON FIRSTNAME (MARY)))

During the previous steps, concept CN2 did not get into the ACTOR slot because of the semantic violations and the OBJECT slot because of the syntactic violations. Similarly, CN3 did not fill the ACTOR slot because of syntax and the OBJECT slot because of semantics. However, from the properties of the concept REPAIR, we know that the filler of the ACTOR slot must be a human and the filler of the OBJECT slot must be a physical object. On the basis of these properties alone, we can put CN2 and CN3 into the right slots. This is done at the last step. Of course, there is a price to pay for ignorance. Without knowledge of the passive, the sentence JOHN WAS HIT BY MARY may be understood incorrectly.

The flexibility of the framework described in this chapter is due to the combination of predictive mechanisms with expert modules. Expectations are the main driving force of this type of conceptual analyzer. They control the appli cation of contextual knowledge accessed during the analysis. Expert modules embody the analyzer's general knowledge about major natural-language constructions. They take over control when the predictive mechanisms fail to account for a piece of input. This framework enables us to combine limited knowledge of the language with our model of the text topic to extract a reasonable amount of information from natural-language texts.


REFERENCES

Birnbaum L. A., & Selfridge M. G. Problems in conceptual analysis of Platural language (Computer Science Research Report No. 168). Yale University, New Haven, October 1979.

Carbonell J. G. Subjective understanding: Computer models of belief systems (Computer Science Research Report No. 150). Yale University, New Haven, January 1979.

DeJong G. F. Skimming stories in real time: An experiment in integrated understanding (Computer Science Research Report No. 150). Yale University, New Haven, May 1979.

Gershman A. V. "Conceptual analysis of noun groups in english". Proceedings of the 5th International Joint Conference on Artificial Intelligence. Cambridge, Mass., August 1977.

Gershman A. . Knowledge-based parsing (Computer Science Research Report No. 156). Yale University, New Haven, April 1979.

Gershman A. V. Automatic yellow pages assistant-a preliminary report (TM 80-1274-14). Bell Laboratories, October 1980.

Ginsparg J. M. Natural language processing in an automatic programming domain. Doctoral Dissertation (Computer Science Department Report STAN-CS-78-671). Stanford University, Stanford, Calif., June 1978.

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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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