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Foundations of statistical natural language processing

Foundations of statistical natural language processing (21회 대출)

자료유형
단행본
개인저자
Manning, Christopher D. Schütze, Hinrich.
서명 / 저자사항
Foundations of statistical natural language processing / Christopher D. Manning, Hinrich Schütze.
발행사항
Cambridge, Mass. :   MIT Press,   c1999.  
형태사항
xxxvii, 680 p. ; 24 cm.
ISBN
0262133601
서지주기
Includes bibliographical references (p. 611-655) and index.
일반주제명
Computational linguistics --Statistical methods.
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010 ▼a 99021137
020 ▼a 0262133601
040 ▼a DLC ▼c DLC ▼d UKM ▼d 211009
049 ▼l 111146434
050 0 0 ▼a P98.5.S83 ▼b M36 1999
082 0 0 ▼a 410/.285 ▼2 21
084 ▼a 410.285 ▼2 DDCK
090 ▼a 410.285 ▼b M283f
100 1 ▼a Manning, Christopher D.
245 1 0 ▼a Foundations of statistical natural language processing / ▼c Christopher D. Manning, Hinrich Schütze.
260 ▼a Cambridge, Mass. : ▼b MIT Press, ▼c c1999.
300 ▼a xxxvii, 680 p. ; ▼c 24 cm.
504 ▼a Includes bibliographical references (p. 611-655) and index.
650 0 ▼a Computational linguistics ▼x Statistical methods.
700 1 ▼a Schütze, Hinrich.

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고6층/ 청구기호 410.285 M283f 등록번호 111146434 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차


CONTENTS

Ⅰ Preliminaries = 1

 1 Introduction = 3

 2 Mathematical Foundations = 39

 3 Linguistic Essentials = 81

 4 Corpus-Based Work = 117

Ⅱ Words = 149

 5 Collocations = 151

 6 Statistical Inference : n-gram Models over Sparse Data = 191

 7 Word Sense Disambiguation = 229

 8 Lexical Acquisition = 265

Ⅲ Grammar = 315

 9 Markov Models = 317

 10 Part-of-Speech Tagging = 341

 11 Probabilistic Context Free Grammars = 381

 12 Probabilistic Parsing = 407

Ⅳ Applications and Techniques = 461

 13 Statistical Alignment and Machine Translation = 463

 14 Clustering = 495

 15 Topics in Information Retrieval = 529

 16 Text Categorization = 575

List of Tables = xv

List of Figures = xxi

Table of Notations = xxv

Preface = xxix

Road Map = xxxv

Ⅰ Preliminaries = 1

 1 Introduction = 3

  1.1 Rationalist and Empiricist Approaches to Language = 4

  1.2 Scientific Content = 7

   1.2.1 Questions that linguistics should answer = 8

   1.2.2 Non-categorical phenomena in language = 11

   1.2.3 Language and cognition as probabilistic phenomena = 15

  1.3 The Ambiguity of Language : Why NLP Is Difficult = 17

  1.4 Dirty Hands = 19

   1.4.1 Lexical resources = 19

   1.4.2 Word counts = 20

   1.4.3 Zipf's laws = 23

   1.4.4 Collocations = 29

   1.4.5 Concordances = 31

  1.5 Further Reading = 34

  1.6 Exercises = 35

 2 Mathematical Foundations = 39

  2.1 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

  2.2 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 = 72

   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

  2.3 Further Reading = 79

 3 Linguistic Essentials = 81

  3.1 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

  3.2 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

   6.1.3 Building n-gram models = 195

  6.2 Statistical Estimators = 196

   6.2.1 Maximum Likelihood Estimation(MLE) = 197

   6.2.2 Laplace's law, Lidstone's law and the Jeffreys-Perks law = 202

   6.2.3 Held out estimation = 205

   6.2.4 Cross-validation(deleted estimation) = 210

   6.2.5 Good-Turing estimation = 212

   6.2.6 Briefly noted = 216

  6.3 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

  6.4 Conclusions = 224

  6.5 Further Reading = 225

  6.6 Exercises = 225

 7 Word Sense Disambiguation = 229

  7.1 Methodological Preliminaries = 232

   7.1.1 Supervised and unsupervised learning = 232

   7.1.2 Pseudowords = 233

   7.1.3 Upper and lower bounds on performance = 233

  7.2 Supervised Disambiguation = 235

   7.2.1 Bayesian classification 2 = 3 5

   7.2.2 An information-theoretic approach = 239

  7.3 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

  7.4 Unsupervised Disambiguation = 252

  7.5 What Is a Word Sense ? = 256

  7.6 Further Reading = 260

  7.7 Exercises = 262

 8 Lexical Acquisition = 265

  8.1 Evaluation Measures = 267

  8.2 Verb Subcategorization = 271

  8.3 Attachment Ambiguity = 278

   8.3.1 Hindle and Rooth(1993) = 280

   8.3.2 General remarks on PP attachment = 284

  8.4 Selectional Preferences = 288

  8.5 Semantic Similarity = 294

   8.5.1 Vector space measures = 296

   8.5.2 Probabilistic measures = 303

  8.6 The Role of Lexical Acquisition in Statistical NLP = 308

  8.7 Further Reading = 312

Ⅲ Grammar = 315

 9 Markov Models = 317

  9.1 Markov Models = 318

  9.2 Hidden Markov Models = 320

   9.2.1 Why use HMMs ? = 322

   9.2.2 General form of an HMM = 324

  9.3 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

  9.4 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

  9.5 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

   10.3.1 Applying HMMs to POS tagging = 357

   10.3.2 The effect of initialization on HMM training = 359

  10.4 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

  10.5 Other Methods, Other Languages = 370

   10.5.1 Other approaches to tagging = 370

   10.5.2 Languages other than English = 371

  10.6 Tagging Accuracy and Uses of Taggers = 371

   10.6.1 Tagging accuracy = 371

   10.6.2 Applications of tagging = 374

  10.7 Further Reading = 377

  10.8 Exercises = 379

 11 Probabilistic Context Free Grammars = 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 = 396

   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 = 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 = 421

   12.1.6 There's more than one way to do it = 423

   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 = 448

   12.2.3 Dependency-based models = 451

   12.2.4 Discussion = 454

  12.3 Further Reading = 456

  12.4 Exercises = 458

Ⅳ Applications and Techniques = 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

  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 K 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 Text Tiling = 567

  15.6 Further Reading = S70

  15.7 Exercises = 573

 16 Text Categorization = S75

  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 Perceptrons = 597

  16.4 k Nearest Neighbor Classification = 604

  16.5 Further Reading = 607

Tiny Statistical Tables = 609

Bibliography = 611

Index = 657



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