Chapter 1 Introduction to Natural Language Understanding 1
1.1 The Study of Language 1
1.2 Applications of Natural Language Understanding 3
1.3 Evaluating Language Understanding Systems 6
1.4 The Different Levels of Language Analysis 9
1.5 Representations and Understanding 11
1.6 The Organization of Natural LanguageUnderstanding Systems 15
ART Ⅰ YNTACTIC PROCESSING 23
Chapter 2 Linguistic Background: An Outline of English Syntax 23
2.1 Words 23
2.2 The Elements of Simple Noun Phrases 25
2.3 Verb Phrases and Simple Sentences 28
2.4 Noun Phrases Revisited 33
2.5 Adjective Phrases 35
2.6 Adverbial Phrases 35
Chapter 3 Grammars and Parsing 41
3.1 Grammars and Sentence Structure 41
3.2 What Makes a Good Grammar 44
3.3 A Top-Down Parser 47
3.4 A Bottom-Up Chart Parser 53
3.5 Transition Network Grammars 61
3.6 Top-Down Chart Parsing 65
3.7 Finite State Models and Morphological Processing 70
3.8 Grammars and Logic Programming 72
Chapter 4 Features and Augmented Grammars 83
4.1 Feature Systems and Augmented Grammars 83
4.2 Some Basic Feature Systems for English 86
4.3 Morphological Analysis and the Lexicon 90
4.4 A Simple Grammar Using Features 94
4.5 Parsing with Features 98
4.6 Augmented Transition Networks 101
4.7 Definite Clause Grammars 106
4.8 Generalized Feature Systems and Unifiication Grammars 109
Chapter 5 Grammars for Natural Language 123
5.1 Auxiliary Verbs and Verb Phrases 123
5.2 Movement Phenomena in Language 127
5.3 Handling Questions in Context-Free Grammars 132
5.4 Relative Clauses 141
5.5 The Hold Mechanism in ATNs 144
5.6 Gap Threading 148
Chapter 6 Toward Efficient Parsing 159
6.1 Human Preferences in Parsing 159
6.2 Encoding Uncertainty: Shift-Reduce Parsers 163
6.3 A Deterministic Parser 170
6.4 Techniques for Effiicient Encoding of Ambiguity 176
6.5 Partial Parsing 180
Chapter 7 Ambiguity Resolution: Statistical Methods 189
7.1 Basic Probability Theory 189
7.2 Estimating Probabilities 192
7.3 Part-of-Speech Tagging 195
7.4 Obtaining Lexical Probabilities 204
7.5 Probabilistic Context-Free Grammars 209
7.6 Best-First Parsing 213
7.7 A Simple Context-Dependent Best-First Parser 216
PART Ⅱ SEMANTIC INTERPRETATION 227
Chapter8 Semantics and Logical Form 227
8.1 Semantics and Logical Form 227
8.2 Word Senses and Ambiguity 231
8.3 The Basic Logical Form Language 233
8.4 Encoding Ambiguity in the Logical Form 238
8.5 Verbs and States in Logical Form 241
8.6 Thematic Roles 244
8.7 Speech Acts and Embedded Sentences 250
8.8 Defining Semantic Structure: Model Theory 251
Chapter 9 Linking Syntax and Semantics 263
9.1 Semantic Interpretation and Compositionality 263
9.2 A Simple Grammar and Lexicon with Semantic Interpretation 267
9.3 Prepositional Phrases and Verb Phrases 271
9.4 Lexicalized Semantic Interpretation and Semantic Roles 275
9.5 Handling Simple Questions 280
9.6 Semantic Interpretation Using Feature Unifiication 283
9.7 Generating Sentences from Logical Form 286
Chapter 10 Ambiguity Resolution 295
10.1 Selectional Restrictions 295
10.2 Semantic Filtering Using Selectional Restrictions 302
10.3 Semantic Networks 305
10.4 Statistical Word Sense Disambiguation 310
10.5 Statistical Semantic Preferences 314
10.6 Combining Approaches to Disambiguation 318
Chapter 11 Other Strategies for Semantic Interpretation 328
11.1 Grammatical Relations 328
11.2 Semantic Grammars 332
11.3 Template Matching 334
11.4 Semantically Driven Parsing Techniques 341
Chapter 12 Scoping and the Interpretation of Noun Phrases 351
12.1 Scoping Phenomena 351
12.2 Definite Descriptions and Scoping 359
12.3 A Method for Scoping While Parsing 360
12.4 Co-Reference and Binding Constraints 366
12.5 Adjective Phrases 372
12.6 Relational Nouns and Nominalizations 375
12.7 Other Problems in Semantics 378
PART Ⅲ CONTEXT AND WORLD KNOWLEDGE 392
Chapter 13 Knowledge Representation and Reasoning 392
13.1 Knowledge Representation 392
13.2 A Representation Based on FOPC 397
13.3 Frames: Representing Stereotypical Information 400
13.4 Handling Natural Language Quantifiication 404
13.5 Time and Aspectual Classes of Verbs 406
13.6 Automating Deduction in Logic-Based Representations 410
13.7 Procedural Semantics and Question Answering 414
13.8 Hybrid Knowledge Representations 419
Chapter 14 Local Discourse Context and Reference 429
14.1 Defiining Local Discourse Context and Discourse Entities 429
14.2 A Simple Model of Anaphora Based on History Lists 433
14.3 Pronouns and Centering 435
14.4 Defiinite Descriptions 440
14.5 Defiinite Reference and Sets 445
14.6 Ellipsis 449
14.7 Surface Anaphora 455
Chapter 15 Using World Knowledge 465
15.1 Using World Knowledge: Establishing Coherence 465
15.2 Matching Against Expectations 466
15.3 Reference and Matching Expectations 471
15.4 Using Knowledge About Action and Causality 473
15.5 Scripts: Understanding Stereotypical Situations 477
15.6 Using Hierarchical Plans 480
15.7 Action-Effect-Based Reasoning 483
15.8 Using Knowledge About Rational Behavior 490
Chapter 16 Discourse Structure 503
16.1 The Need for Discourse Structure 503
16.2 Segmentation and Cue Phrases 504
16.3 Discourse Structure and Reference 510
16.4 Relating Discourse Structure and Inference 512
16.5 Discourse Structure, Tense, and Aspect 517
16.6 Managing the Attentional Stack 524
16.7 An Example 530
Chapter 17 Defining a Conversational Agent 541
17.1 What’s Necessary to Build a Conversational Agent? 541
17.2 Language as a Multi-Agent Activity 543
17.3 Representing Cognitive States: Beliefs 545
17.4 Representing Cognitive States: Desires,Intentions, and Plans 551
17.5 Speech Acts and Communicative Acts 554
17.6 Planning Communicative Acts 557
17.7 Communicative Acts and the Recognition of Intention 561
17.8 The Source of Intentions in Dialogue 564
17.9 Recognizing Illocutionary Acts 567
17.10 Discourse-Level Planning 570
APPENDIX A An Introduction to Logic and Model-Theoretic Semantics 579
A.1 Logic and Natural Language 579
A.2 Model-Theoretic Semantics 584
A.3 A Semantics for FOPC: Set-Theoretic Models 588
APPENDIX B Symbolic Computation 595
B.1 Symbolic Data Structures 595
B.2 Matching 598
B.3 Search Algorithms 600
B.4 Logic Programming 603
B.5 The Unifiication Algorithm 604
APPENDIX C Speech Recognition and Spoken Language 611
C.1 Issues in Speech Recognition 611
C.2 The Sound Structure of Language 613
C.3 Signal Processing 616
C.4 Speech Recognition 619
C.5 Speech Recognition and Natural Language Understanding 623
C.6 Prosody and Intonation 625
BIBLIOGRAPHY 629
INDEX 645