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Chapter 1 Introduction to Natural Language Understanding1

1.1 The Study of Language1

1.2 Applications of Natural Language Understanding3

1.3 Evaluating Language Understanding Systems6

1.4 The Different Levels of Language Analysis9

1.5 Representations and Understanding11

1.6 The Organization of Natural LanguageUnderstanding Systems15

ART Ⅰ YNTACTIC PROCESSING23

Chapter 2 Linguistic Background: An Outline of English Syntax23

2.1 Words23

2.2 The Elements of Simple Noun Phrases25

2.3 Verb Phrases and Simple Sentences28

2.4 Noun Phrases Revisited33

2.5 Adjective Phrases35

2.6 Adverbial Phrases35

Chapter 3 Grammars and Parsing41

3.1 Grammars and Sentence Structure41

3.2 What Makes a Good Grammar44

3.3 A Top-Down Parser47

3.4 A Bottom-Up Chart Parser53

3.5 Transition Network Grammars61

3.6 Top-Down Chart Parsing65

3.7 Finite State Models and Morphological Processing70

3.8 Grammars and Logic Programming72

Chapter 4 Features and Augmented Grammars83

4.1 Feature Systems and Augmented Grammars83

4.2 Some Basic Feature Systems for English86

4.3 Morphological Analysis and the Lexicon90

4.4 A Simple Grammar Using Features94

4.5 Parsing with Features98

4.6 Augmented Transition Networks101

4.7 Definite Clause Grammars106

4.8 Generalized Feature Systems and Unifiication Grammars109

Chapter 5 Grammars for Natural Language123

5.1 Auxiliary Verbs and Verb Phrases123

5.2 Movement Phenomena in Language127

5.3 Handling Questions in Context-Free Grammars132

5.4 Relative Clauses141

5.5 The Hold Mechanism in ATNs144

5.6 Gap Threading148

Chapter 6 Toward Efficient Parsing159

6.1 Human Preferences in Parsing159

6.2 Encoding Uncertainty: Shift-Reduce Parsers163

6.3 A Deterministic Parser170

6.4 Techniques for Effiicient Encoding of Ambiguity176

6.5 Partial Parsing180

Chapter 7 Ambiguity Resolution: Statistical Methods189

7.1 Basic Probability Theory189

7.2 Estimating Probabilities192

7.3 Part-of-Speech Tagging195

7.4 Obtaining Lexical Probabilities204

7.5 Probabilistic Context-Free Grammars209

7.6 Best-First Parsing213

7.7 A Simple Context-Dependent Best-First Parser216

PART Ⅱ SEMANTIC INTERPRETATION227

Chapter8 Semantics and Logical Form227

8.1 Semantics and Logical Form227

8.2 Word Senses and Ambiguity231

8.3 The Basic Logical Form Language233

8.4 Encoding Ambiguity in the Logical Form238

8.5 Verbs and States in Logical Form241

8.6 Thematic Roles244

8.7 Speech Acts and Embedded Sentences250

8.8 Defining Semantic Structure: Model Theory251

Chapter 9 Linking Syntax and Semantics263

9.1 Semantic Interpretation and Compositionality263

9.2 A Simple Grammar and Lexicon with Semantic Interpretation267

9.3 Prepositional Phrases and Verb Phrases271

9.4 Lexicalized Semantic Interpretation and Semantic Roles275

9.5 Handling Simple Questions280

9.6 Semantic Interpretation Using Feature Unifiication283

9.7 Generating Sentences from Logical Form286

Chapter 10 Ambiguity Resolution295

10.1 Selectional Restrictions295

10.2 Semantic Filtering Using Selectional Restrictions302

10.3 Semantic Networks305

10.4 Statistical Word Sense Disambiguation310

10.5 Statistical Semantic Preferences314

10.6 Combining Approaches to Disambiguation318

Chapter 11 Other Strategies for Semantic Interpretation328

11.1 Grammatical Relations328

11.2 Semantic Grammars332

11.3 Template Matching334

11.4 Semantically Driven Parsing Techniques341

Chapter 12 Scoping and the Interpretation of Noun Phrases351

12.1 Scoping Phenomena351

12.2 Definite Descriptions and Scoping359

12.3 A Method for Scoping While Parsing360

12.4 Co-Reference and Binding Constraints366

12.5 Adjective Phrases372

12.6 Relational Nouns and Nominalizations375

12.7 Other Problems in Semantics378

PART Ⅲ CONTEXT AND WORLD KNOWLEDGE392

Chapter 13 Knowledge Representation and Reasoning392

13.1 Knowledge Representation392

13.2 A Representation Based on FOPC397

13.3 Frames: Representing Stereotypical Information400

13.4 Handling Natural Language Quantifiication404

13.5 Time and Aspectual Classes of Verbs406

13.6 Automating Deduction in Logic-Based Representations410

13.7 Procedural Semantics and Question Answering414

13.8 Hybrid Knowledge Representations419

Chapter 14 Local Discourse Context and Reference429

14.1 Defiining Local Discourse Context and Discourse Entities429

14.2 A Simple Model of Anaphora Based on History Lists433

14.3 Pronouns and Centering435

14.4 Defiinite Descriptions440

14.5 Defiinite Reference and Sets445

14.6 Ellipsis449

14.7 Surface Anaphora455

Chapter 15 Using World Knowledge465

15.1 Using World Knowledge: Establishing Coherence465

15.2 Matching Against Expectations466

15.3 Reference and Matching Expectations471

15.4 Using Knowledge About Action and Causality473

15.5 Scripts: Understanding Stereotypical Situations477

15.6 Using Hierarchical Plans480

15.7 Action-Effect-Based Reasoning483

15.8 Using Knowledge About Rational Behavior490

Chapter 16 Discourse Structure503

16.1 The Need for Discourse Structure503

16.2 Segmentation and Cue Phrases504

16.3 Discourse Structure and Reference510

16.4 Relating Discourse Structure and Inference512

16.5 Discourse Structure, Tense, and Aspect517

16.6 Managing the Attentional Stack524

16.7 An Example530

Chapter 17 Defining a Conversational Agent541

17.1 What’s Necessary to Build a Conversational Agent?541

17.2 Language as a Multi-Agent Activity543

17.3 Representing Cognitive States: Beliefs545

17.4 Representing Cognitive States: Desires,Intentions, and Plans551

17.5 Speech Acts and Communicative Acts554

17.6 Planning Communicative Acts557

17.7 Communicative Acts and the Recognition of Intention561

17.8 The Source of Intentions in Dialogue564

17.9 Recognizing Illocutionary Acts567

17.10 Discourse-Level Planning570

APPENDIX A An Introduction to Logic and Model-Theoretic Semantics579

A.1 Logic and Natural Language579

A.2 Model-Theoretic Semantics584

A.3 A Semantics for FOPC: Set-Theoretic Models588

APPENDIX B Symbolic Computation595

B.1 Symbolic Data Structures595

B.2 Matching598

B.3 Search Algorithms600

B.4 Logic Programming603

B.5 The Unifiication Algorithm604

APPENDIX C Speech Recognition and Spoken Language611

C.1 Issues in Speech Recognition611

C.2 The Sound Structure of Language613

C.3 Signal Processing616

C.4 Speech Recognition619

C.5 Speech Recognition and Natural Language Understanding623

C.6 Prosody and Intonation625

BIBLIOGRAPHY629

INDEX645