醉染图书词义消歧9787301249536

导读 1
Contributors 16
Foreword 19
Preface
1 Introduction 1
Eneko Agirre and Philip Edmonds
1.1 Word Sense Disambiguation 1
1.2 A Brief History of WSD Research 4
1.3 What is a Word Sense？ 8
1.4 Applications of WSD 10
1.5 Basic Approaches to WSD 12
1.6 State-of-the-Art Performance 14
1.7 Promising Directions 15
1.8 Overview of This Book 19
1.9 Further Reading 21
References 22
2 Word Senses 29
Adam Kilgarriff
2.1 Introduction 29
2.2 Lexicographers 30
. Philosophy 32
..1 Meaning is Something You Do 32
..2 The Fregean Tradition and Reification 33
.. Two Incompatible Semantics？ 33
..4 Implications for Word Senses 34
2.4 Lexicalization 35
2.5 Corpus Evidence 39
2.5.1 Lexicon Size 41
2.5.2 ottions 42
2.6 Conclusion 43
2.7 Further Reading 44
Acknowledgments 45
References 45
3 Making Sense About Sense 47
Nancy Ide and Yorick Wilks
3.1 Introduction 47
3.2 WSD and the Lexicographers 49
3.3 WSD and Sense Inventories 51
3.4 NLP Applications and WSD 55
3.5 What Level of Sense Distinctions Do We Need for NLP， If Any？ 58
3.6 What Now for WSD？ 64
3.7 Conclusion 68
References 68
4 Evaluation of WSD Systems 75
Martha Palmer， Hwee Tou Ng and Hoa Trang Dang
4.1 Introduction 75
4.1.1 Terminology 76
4.1.2 Overview 80
4.2 Background 81
4.2.1 WordNet and Semcor 81
4.2.2 The Line and Interest Corpora 83
4.. The DSO Corpus 84
4.2.4 Open Mind Word Expert 85
4.3 Evaluation Using Pseudo-Words 86
4.4 Senseval Evaluation Exercises 86
4.4.1 Senseval-187
Evaluation and Scoring 88
4.4.2 Senseval-288
English All-Words Task 89
English Lexical Sample Task 89
4.4.3 Comparison of Tagging Exercises 91
4.5 Sources of Inter-Annotator Disagreement 92
4.6 Granularity of Sense： Groupings for WordNet 95
4.6.1 Criteria for WordNet Sense Grouping 96
4.6.2 Analysis of Sense Grouping 97
4.7 Senseval-398
4.8 Discussion 99
References 102
5 Knowledge-Based Methods for WSD 107
Rada Mihalcea
5.1 Introduction 107
5.2 Lesk Algorithm 108
5.2.1 Variations of the Lesk Algorithm 110
Simulated Annealing 110
Simplified Lesk Algorithm 111
Augmented Semantic Spaces 113
Summary 113
5.3 Semantic Similarity 114
5.3.1 Measures of Semantic Similarity 114
5.3.2 Using Semantic Similarity Within a Local Context 117
5.3.3 Using Semantic Similarity Within a Global Context 118
5.4 Selectional Preferences 119
5.4.1 Preliminaries： Learning Word-to-Word Relations 120
5.4.2 Learning Selectional Preferences 120
5.4.3 Using Selectional Preferences 122
5.5 Heuristics for Word Sense Disambiguation 1
5.5.1 Most Frequent Sense 1
5.5.2 One Sense Per Discourse 124
5.5.3 One Sense Per Collocation 124
5.6 Knowledge-Based Methods at Senseval-2125
5.7 Conclusions 126
References 127
6 Unsupervised Corpus-Based Methods for WSD 133
Ted Pedersen
6.1 Introduction 133
6.1.1 Scope 134
6.1.2 Motivation 136
Distributional Methods 137
Translational Equivalence 139
6.1.3 Approaches 140
6.2 Type-Based Discrimination 141
6.2.1 Representation of Context 142
6.2.2 Algorithms 145
Latent Semantic Analysis （LSA） 146
Hyperspace Analogue to Language （HAL） 147
Clustering By Committee （CBC） 148
6.. Discussion 150
6.3 Token-Based Discrimination 150
6.3.1 Representation of Context 151
6.3.2 Algorithms 151
Context Group Discrimination 152
Mcitty’s Similarity Analysis 154
6.3.3 Discussion 157
6.4 Translational Equivalence 158
6.4.1 Representation of Context 159
6.4.2 Algorithms 159
6.4.3 Discussion 160
6.5 Conclusions and the Way Forward 161
Acknowledgments 162
References 162
7 Supervised Corpus-Based Methods for WSD 167
Lluís M？？rquez， Gerard Escudero， David Martínez and German Rigau
7.1 Introduction to Supervised WSD 167
7.1.1 Machine Learning for Classification 168
An Example on WSD 170
7.2 A Survey of Supervised WSD 171
7.2.1 Main Corpora Used 172
7.2.2 Main Sense Repositories 173
7.. Representation of Examples by Means of Features 174
7.2.4 Main Approaches to Supervised WSD 175
Probabilistic Methods 175
Methods Based on the Similarity of the Examples 176
Methods Based on Discriminating Rules 177
Methods Based on Rule Combination 179
Linear Classifiers and Kernel-Based Approaches 179
Discourse Properties： The Yarowsky Bootstrapping Algorithm 181
7.2.5 Supervised Systems in the Senseval Evaluations 183
7.3 An Empirical Study of Supervised Algorithms for WSD 184
7.3.1 Five Learning Algorithms Under Study 185
Na？ve Bayes （NB） 185
Exemplar-Based Learning （kNN） 186
Decision Lists （DL） 187
AdaBoost （AB） 187
Support Vector Machines （SVM） 189
7.3.2 Empirical Evaluation on the DSO Corpus 190
Experiments 191
7.4 Current Challenges of the Supervised Approach 195
7.4.1 Right-Sized Training Sets 195
7.4.2 Porting Across Corpora 196
7.4.3 The Knowledge Acquisition Bottleneck 197
Automatic Acquisition of Training Examples 198
Active Learning 199
Combining Training Examples from Different Words 199
Parallel Corpora 200
7.4.4 Bootstrapping 201
7.4.5 Feature Selection and Parameter Optimization 202
7.4.6 Combination o Aorithms and Knowledge Sources 203
7.5 Conclusions and Future Trends 205
Acknowledgments 206
References 207
8 Knowledge Sources for WSD 217
Eneko Agirre and Mark Stevenson
8.1 Introduction 217
8.2 Knowledge Sources Relevant to WSD 218
8.2.1 Syntactic 219
Part of Speech （KS 1） 219
Morphology （KS 2） 219
Collocations （KS 3） 220
Subcategorization （KS 4） 220
8.2.2 Semantic 220
Frequency of Senses （KS 5） 220
Semantic Word Associations （KS 6） 221
Selectional Preferences （KS 7） 221
Semantic Roles （KS 8） 222
8.. Pragmatic／Topical 222
Domain （KS 9） 222
Topical Word Association （KS 10） 222
Pragmatics （KS 11） 2
8.3 Features and Lexical Resources 2
8.3.1 Target-Word Specific Features 224
8.3.2 Local Features 225
8.3.3 Global Features 227
8.4 Identifying Knowledge Sources in Actual Systems 228
8.4.1 Senseval-2 Systems 229
8.4.2 Senseval-3 Systems 1
8.5 Comparison of Experimental Results 1
8.5.1 Senseval Results 2
8.5.2 Yarowsky and Florian （2002）
8.5.3 Lee and Ng （2002） 4
8.5.4 Martínez et al.（2002）
8.5.5 Agirre and Martínez （2001 a）
8.5.6 Stevenson and Wilks （2001） 240
8.6 Discussion 242
8.7 Conclusions 245
Acknowledgments 246
References 247
9 Automatic Acquisition of Lexical Information and Examples 253
Julio Gonzalo and Felisa Verdejo
9.1 Introduction 253
9.2 Mining Topical Knowledge About Word Senses 254
9.2.1 Topic Signatures 255
9.2.2 Association of Web Directories to Word Senses 257
9.3 Automatic Acquisition of Sense-Tagged Corpora 258
9.3.1 Acquisition by Direct Web Searching 258
9.3.2 Bootstrapping from Seed Examples 261
9.3.3 Acquisition via Web Directories 263
9.3.4 Acquisition via Cross-Language Evidence 264
9.3.5 Web-Based Cooperative Annotation 268
9.4 Discussion 269
Acknowledgments 271
References 272
10 Domain-Specific WSD 275
Paul Buitelaar， Bernardo Magnini， Carlo Strapparava and Piek Vossen
10.1 Introduction 275
10.2 Approaches to Domain-Specific WSD 277
10.2.1 Subject Codes 277
10.2.2 Topic Signatures and Topic Variation 282
Topic Signatures 282
Topic Variation 283
10.. Domain Tuning 284
Top-down Domain Tuning 285
Bottom-up Domain Tuning 285
10.3 Domain-Specific Disambiguation in Applications 288
10.3.1 User-Modeling for Recommender Systems 288
10.3.2 Cross-Lingual Information Retrieval 289
10.3.3 The MEANING Project 292
10.4 Conclusions 295
References 296
11 WSD in NLP Applications 299
Philip Resnik
11.1 Introduction 299
11.2 Why WSD？ 300
Argument from Faith 300
Argument by Analogy 301
Argument from Specific Applications 302
11.3 Traditional WSD in Applications 303
11.3.1 WSD in Traditional Information Retrieval 304
11.3.2 WSD in Applications Related to Information Retrieval 307
Cross-Language IR 308
estion Answering 309
Document Classification 312
11.3.3 WSD in Traditional Machine Translation 313
11.3.4 Sense Ambiguity in Statistical Machine Translation 315
11.3.5 Other Emerging Applications 317
11.4 Alternative Conceptions of Word Sense 320
11.4.1 Richer Linguistic Representations 320
11.4.2 Patterns of Usage 321
11.4.3 Cross-Language Relationships 3
11.5 Conclusions 325
Acknowledgments 325
References 326
A Resources for WSD 339
A.1 Sense Inventories 339
A.1.1 Dictionaries 339
A.1.2 Thesauri 341
A.1.3 Lexical Knowledge Bases 341
A.2 Corpora 343
A.2.1 Raw Corpora 343
A.2.2 Sense-Tagged Corpora 345
A.. Automatically Tagged Corpora 347
A.3 Other Resources 348
A.3.1 Software 348
A.3.2 Utilities， Demos， and Data 349
A.3.3 Language Data Providers 350
A.3.4 Organizations and Mailing Lists 350
Index of Terms 353
Index of Authors and Algorithms 361

艾吉瑞，西班牙国立巴斯克大学副教授。

《词义消歧——算法与应用(英文）》是本全面探讨词义消歧的书，对于重要的算法、方式、指标、结果、哲学问题和应用，都有涉略，并有这个领域的不错学者对本领域的历史及发展所做的较为全面的综述。研究者可以从本书了解到本领域的成果和发展趋势，开发人员可以从本书了解一些技术和方法。