Foundations of statistical natural language processing:
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cambridge, Mass. [u.a.]
MIT Press
2003
|
| Edition: | 6. print with corr. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Hier auch später erschienene, unveränderte Nachdrucke |
| Physical Description: | XXXVII, 680 S. graph. Darst. |
| ISBN: | 0262133601 9780262133609 |
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Record in the Search Index
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Brief Contents I Preliminaries 1 2 3 4 Introduction 3 Mathematical Foundations Linguistic Essentials 81 Corpus-Based Work 117 II Words 5 6 7 8 39 149 Collocations 151 Statistical Inference: ո-gram Models over Sparse Data Word Sense Disambiguation 229 Lexical Acguisition 265 III Grammar 9 10 11 12 1 315 Markov Models 317 Part-of-Speech Tagging 341 Probabilistic Context Free Grammars Probabilistic Parsing 407 ГѴ Applications and Techniques 13 14 15 16 381 461 Statistical Alignment and Machine Translation Clustering 495 Topics in Information Retrieval 529 Text Categorization 575 463 191
Contents List of Tables xv List of Figures xxi Table of Notations Preface Road Map xxv xxix xxxv I Preliminaries 1 1 Introduction 3 1.1 Rationalist and Empiricist Approaches to Language 1.2 Scientific Content 7 1.2.1 Questions that linguisticsshould answer 1.2.2 Non-categorical phenomena in language 1.2.3 Language and cognition as probabilistic phenomena 15 1.3 The Ambiguity of Language: Why NLP Is Difficult 1.4 Dirty Hands 19 1.4.1 Lexical resources 19 1.4.2 Word counts 20 1.4.3 Zipfs laws 23 1.4.4 Collocations 29 1.4.5 Concordances 31 1.5 Further Reading 34
viii Contents 1.6 Exercises 35 2 Mathematical Foundations 2.1 2.2 2.3 Elementary Probability Theory 40 2.1.1 Probability spaces 40 2.1.2 Conditional probability and independence 42 2.1.3 Bayes’ theorem 43 2.1.4 Random variables 45 2.1.5 Expectation and variance 46 2.1.6 Notation 47 2.1.7 Joint and conditional distributions 48 2.1.8 Determining P 48 2.1.9 Standard distributions 50 2.1.10 Bayesian statistics 54 2.1.11 Exercises 59 Essential Information Theory 60 2.2.1 Entropy 61 2.2.2 Joint entropy and conditional entropy 63 2.2.3 Mutual information 66 2.2.4 The noisy channel model 68 2.2.5 Relative entropy or Kullback-Leibler divergence 2.2.6 The relation to language: Cross entropy 73 2.2.7 The entropy of English 76 2.2.8 Perplexity 78 2.2.9 Exercises 78 Further Reading 79 3 Linguistic Essentials 3.1 3.2 39 81 Parts of Speech and Morphology 81 3.1.1 Nouns and pronouns 83 3.1.2 Words that accompany nouns: Determiners and adjectives 87 3.1.3 Verbs 88 3.1.4 Other parts of speech 91 Phrase Structure 93 3.2.1 Phrase structure grammars 96 3.2.2 Dependency: Arguments and adjuncts 101 3.2.3 X' theory 106 3.2.4 Phrase structure ambiguity 107 72
Contents 3.3 3.4 3.5 3.6 їх Semantics and Pragmatics Other Areas 112 Further Reading 113 Exercises 114 4 Corpus-Based Work 4.1 4.3 4.4 4.5 109 117 Getting Set Up 118 4.1.1 Computers 118 4.1.2 Corpora 118 4.1.3 Software 120 Looking at Text 123 4.2.1 Low-level formatting issues 123 4.2.2 Tokenization: What is a word? 124 4.2.3 Morphology 131 4.2.4 Sentences 134 Marked-up Data 136 4.3.1 Markup schemes 137 4.3.2 Grammatical tagging 139 Further Reading 145 Exercises 147 II Words 149 5 Collocations 151 5.1 Frequency 153 5.2 Mean and Variance 157 5.3 Hypothesis Testing 162 5.3.1 The t test 163 5.3.2 Hypothesis testingof differences 5.3.3 Pearson’s chi-square test 169 5.3.4 Likelihood ratios 172 5.4 Mutual Information 178 5.5 The Notion of Collocation 183 5.6 Further Reading 187 166 6 Statistical Inference: ո-gram Models over Sparse Data 6.1 Bins: Forming Equivalence Classes 192 6.1.1 Reliability vs. discrimination 192 6.1.2 ո-gram models 192 191
x Contents 6.2 6.3 6.4 6.5 6.6 6.1.3 Building n-gram models 195 Statistical Estimators 196 6.2.1 Maximum Likelihood Estimation (MLE) 6.2.2 Laplace’s law, Lidstone’s law and the 202 Jeffreys-Perks law 6.2.3 Held out estimation 205 6.2.4 Cross-validation (deleted estimation) 6.2.5 Good-Turing estimation 212 6.2.6 Briefly noted 216 Combining Estimators 217 6.3.1 Simple linear interpolation 218 6.3.2 Katz’s backing-off 219 6.3.3 General linear interpolation 220 6.3.4 Briefly noted 222 6.3.5 Language models for Austen 223 Conclusions 224 Further Reading 225 Exercises 225 7 Word Sense Disambiguation 7.1 7.2 7.3 7.4 7.5 7.6 7.7 197 210 229 Methodological Preliminaries 232 7.1.1 Supervised and unsupervised learning 232 7.1.2 Pseudowords 233 7.1.3 Upper and lower bounds onperformance 233 Supervised Disambiguation 235 7.2.1 Bayesian classification 235 7.2.2 An information-theoretic approach 239 Dictionary-Based Disambiguation 241 7.3.1 Disambiguation based on sense definitions 242 7.3.2 Thesaurus-based disambiguation 244 7.3.3 Disambiguation based on translations in a second-language corpus 247 7.3.4 One sense per discourse, one sense per collocation 249 Unsupervised Disambiguation 252 What Is a Word Sense? 256 Further Reading 260 Exercises 262
Contents Xl 8 Lexical Acquisition 8.1 8.2 8.3 8.4 8.5 8.6 8.7 III Evaluation Measures 267 Verb Subcategorization 271 Attachment Ambiguity 278 8.3.1 Hindié and Rooth (1993) 280 8.3.2 General remarks on PP attachment 284 Selecţionai Preferences 288 Semantic Similarity 294 8.5.1 Vector space measures 296 8.5.2 Probabilistic measures 303 The Role of Lexical Acquisition in Statistical NLP 308 Further Reading 312 Grammar 9 Markov Models 9.1 9.2 9.3 9.4 9.5 265 315 317 Markov Models 318 Hidden Markov Models 320 9.2.1 WhyuseHMMs? 322 9.2.2 General form of an HMM 324 The Three Fundamental Questions for HMMs 325 9.3.1 Finding the probability of an observation 326 9.3.2 Finding the best state sequence 331 9.3.3 The third problem: Parameter estimation 333 HMMs: Implementation,Properties, and Variants 336 9.4.1 Implementation 336 9.4.2 Variants 337 9.4.3 Multiple input observations 338 9.4.4 Initialization of parameter values 339 Further Reading 339 10 Part-of-Speech Tagging 341 10.1 The Information Sources in Tagging 343 10.2 Markov Model Taggers 345 10.2.1 The probabilistic model 345 10.2.2 The Viterbi algorithm 349 10.2.3 Variations 351 10.3 Hidden Markov Model Taggers 356
Contents xii 10.4 10.5 10.6 10.7 10.8 10.3.1 Applying HMMs to POS tagging 357 10.3.2 The effect of initialization on HMM training Transformation-Based Learning of Tags 361 10.4.1 Transformations 362 10.4.2 The learning algorithm 364 10.4.3 Relation to other models 365 10.4.4 Automata 367 10.4.5 Summary 369 Other Methods, Other Languages 370 10.5.1 Other approaches to tagging 370 10.5.2 Languages other than English 371 Tagging Accuracy and Uses of Taggers 371 10.6.1 Tagging accuracy 371 10.6.2 Applications of tagging 374 Further Reading 377 Exercises 379 11 Probabilistic Context Free Grammars 359 381 11.1 Some Features of PCFGs 386 11.2 Questions for PCFGs 388 11.3 The Probability of a String 392 11.3.1 Using inside probabilities 392 11.3.2 Using outside probabilities 394 11.3.3 Finding the most likely parse for a sentence 11.3.4 Training a PCFG 398 11.4 Problems with the Inside-Outside Algorithm 401 11.5 Further Reading 402 11.6 Exercises 404 12 Probabilistic Parsing 396 407 12.1 Some Concepts 408 12.1.1 Parsing for disambiguation 408 12.1.2 Treebanks 412 12.1.3 Parsing models vs. language models 414 12.1.4 Weakening the independence assumptions of PCFGs 416 12.1.5 Tree probabilities and derivational probabilities 12.1.6 There’s more than one way to do it 423 421
Contents 12.1.7 Phrase structure grammars and dependency grammars 428 12.1.8 Evaluation 431 12.1.9 Equivalent models 437 12.1.10 Building parsers: Search methods 439 12.1.11 Use of the geometric mean 442 12.2 Some Approaches 443 12.2.1 Non-lexicalized treebank grammars 443 12.2.2 Lexicalized models using derivational histories 12.2.3 Dependency-based models 451 12.2.4 Discussion 454 12.3 Further Reading 456 12.4 Exercises 458 ГѴ Applications and Techniques 4 461 13 Statistical Alignment and Machine Translation 463 13.1 Text Alignment 466 13.1.1 Aligning sentences and paragraphs 467 13.1.2 Length-based methods 471 13.1.3 Offset alignment by signal processing techniques 475 13.1.4 Lexical methods of sentence alignment 478 13.1.5 Summary 484 13.1.6 Exercises 484 13.2 Word Alignment 484 13.3 Statistical Machine Translation 486 13.4 Further Reading 492 14 Clustering 495 14.1 Hierarchical Clustering 500 14.1.1 Single-link and complete-link clustering 503 14.1.2 Group-average agglomerative clustering 507 14.1.3 An application: Improving a language model 509 14.1.4 Top-down clustering 512 14.2 Non-Hierarchical Clustering 514 14.2.1 K-means 515 14.2.2 The EM algorithm 518 14.3 Further Reading 527
Contents XIV 14.4 Exercises 528 15 Topics in Information Retrieval 529 15.1 Some Background on Information Retrieval 530 15.1.1 Common design features of IR systems 532 15.1.2 Evaluation measures 534 15.1.3 The probability ranking principle (PRP) 538 15.2 The Vector Space Model 539 15.2.1 Vector similarity 540 15.2.2 Term weighting 541 15.3 Term Distribution Models 544 15.3.1 The Poisson distribution 545 15.3.2 The two-Poisson model 548 15.3.3 The К mixture 549 15.3.4 Inverse document frequency 551 15.3.5 Residual inverse document frequency 553 15.3.6 Usage of term distribution models 554 15.4 Latent Semantic Indexing 554 15.4.1 Least-squares methods 557 15.4.2 Singular Value Decomposition 558 15.4.3 Latent Semantic Indexing in IR 564 15.5 Discourse Segmentation 566 15.5.1 TextTiling 567 15.6 Further Reading 570 15.7 Exercises 573 16 Text Categorization 575 16.1 Decision Trees 578 16.2 Maximum Entropy Modeling 589 16.2.1 Generalized iterative scaling 591 16.2.2 Application to text categorization 594 16.3 Percep trons 597 16.4 к Nearest Neighbor Classification 604 16.5 Further Reading 607 Tiny Statistical Tables Bibliography Index 657 611 609 |
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| spelling | Manning, Christopher D. 1965- Verfasser (DE-588)172764629 aut Foundations of statistical natural language processing Christopher D. Manning ; Hinrich Schütze 6. print with corr. Cambridge, Mass. [u.a.] MIT Press 2003 XXXVII, 680 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Computational linguistics Statistical methods Natürliche Sprache (DE-588)4041354-8 gnd rswk-swf Sprachverarbeitung (DE-588)4116579-2 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Computerlinguistik (DE-588)4035843-4 gnd rswk-swf Sprachstatistik (DE-588)4182534-2 gnd rswk-swf Natürliche Sprache (DE-588)4041354-8 s Sprachstatistik (DE-588)4182534-2 s Computerlinguistik (DE-588)4035843-4 s DE-188 Sprachverarbeitung (DE-588)4116579-2 s Statistik (DE-588)4056995-0 s 1\p DE-604 Schütze, Hinrich 1964- Verfasser (DE-588)112281672 aut Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010591577&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Manning, Christopher D. 1965- Schütze, Hinrich 1964- Foundations of statistical natural language processing Computational linguistics Statistical methods Natürliche Sprache (DE-588)4041354-8 gnd Sprachverarbeitung (DE-588)4116579-2 gnd Statistik (DE-588)4056995-0 gnd Computerlinguistik (DE-588)4035843-4 gnd Sprachstatistik (DE-588)4182534-2 gnd |
| subject_GND | (DE-588)4041354-8 (DE-588)4116579-2 (DE-588)4056995-0 (DE-588)4035843-4 (DE-588)4182534-2 |
| title | Foundations of statistical natural language processing |
| title_auth | Foundations of statistical natural language processing |
| title_exact_search | Foundations of statistical natural language processing |
| title_full | Foundations of statistical natural language processing Christopher D. Manning ; Hinrich Schütze |
| title_fullStr | Foundations of statistical natural language processing Christopher D. Manning ; Hinrich Schütze |
| title_full_unstemmed | Foundations of statistical natural language processing Christopher D. Manning ; Hinrich Schütze |
| title_short | Foundations of statistical natural language processing |
| title_sort | foundations of statistical natural language processing |
| topic | Computational linguistics Statistical methods Natürliche Sprache (DE-588)4041354-8 gnd Sprachverarbeitung (DE-588)4116579-2 gnd Statistik (DE-588)4056995-0 gnd Computerlinguistik (DE-588)4035843-4 gnd Sprachstatistik (DE-588)4182534-2 gnd |
| topic_facet | Computational linguistics Statistical methods Natürliche Sprache Sprachverarbeitung Statistik Computerlinguistik Sprachstatistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010591577&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT manningchristopherd foundationsofstatisticalnaturallanguageprocessing AT schutzehinrich foundationsofstatisticalnaturallanguageprocessing |