Verfügbarkeit: Speech and language processing

Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition

This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Cov...

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Bibliographische Detailangaben
Hauptverfasser:	Jurafsky, Dan 1962- (VerfasserIn), Martin, James H. 1959- (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Upper Saddle River Pearson Education International, Prentice Hall 2009
Ausgabe:	2. ed., internat. ed.
Schriftenreihe:	Prentice-Hall-series in artificial intelligence
Schlagworte:	Automatic speech recognition Computational linguistics Parsing (Computer grammar) Automatische Spracherkennung Computerlinguistik Digitale Sprachverarbeitung Natürlichsprachiges System
Online-Zugang:	Inhaltsverzeichnis
Zusammenfassung:	This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.
Beschreibung:	Hier auch später erschienene, unveränderte Nachdrucke
Beschreibung:	1024 S. Ill., graph. Darst.
ISBN:	0135041961 9780135041963

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650		4	\|a Automatic speech recognition
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Datensatz im Suchindex

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adam_text	Contents Foreword 23 Preface 25 About the Authors 31 1 Introduction 35 1.1 Knowledge in Speech and Language Processing ........... 36 1.2 Ambiguity ............................... 38 1.3 Models and Algorithms ........................ 39 1.4 Language, Thought, and Understanding ................ 40 1.5 The State of the Art .......................... 42 1.6 Some Brief History .......................... 43 1.6.1 Foundational Insights: 1940s and 1950s........... 43 1.6.2 The Two Camps: 1957-1970................. 44 1.6.3 Four Paradigms: 1970-1983................. 45 1.6.4 Empiricism and Finite-State Models Redux: 1983-1993 .. 46 1.6.5 The Field Comes Together: 1994-1999........... 46 1.6.6 The Rise of Machine Learning: 2000-2008......... 46 1.6.7 On Multiple Discoveries ................... 47 1.6.8 A Final Brief Note on Psychology .............. 48 1.7 Summary ................................ 48 Bibliographical and Historical Notes ..................... 49 1 Words 2 Regular Expressions and Automata 51 2.1 Regular Expressions .......................... 51 2.1.1 Basic Regular Expression Patterns .............. 52 2.1.2 Disjunction, Grouping, and Precedence ........... 55 2.1.3 A Simple Example ...................... 56 2.1.4 A More Complex Example .................. 57 2.1.5 Advanced Operators ..................... 58 2.1.6 Regular Expression Substitution, Memory, and ELIZA ... 59 2.2 Finite-State Automata ......................... 60 2.2.1 Use of an FSA to Recognize Sheeptalk ........... 61 2.2.2 Formal Languages ...................... 64 2.2.3 Another Example ....................... 65 2.2.4 Non-Deterministic FSAs ................... 66 2.2.5 Use of an NFSA to Accept Strings .............. 67 2.2.6 Recognition as Search .................... 69 2.2.7 Relation of Deterministic and Non-Deterministic Automata 72 2.3 Regular Languages and FSAs ..................... 72 2.4 Summary ................................ 75 10 Contents Bibliographical and Historical Notes ..................... 76 Exercises ................................... 76 Words and Transducers 79 3.1 Survey of (Mostly) English Morphology ............... 81 3.1.1 Inflectional Morphology ................... 82 3.1.2 Derivational Morphology ................... 84 3.1.3 Cliticization .......................... 85 3.1.4 Non-Concatenative Morphology ............... 85 3.1.5 Agreement .......................... 86 3.2 Finite-State Morphological Parsing .................. 86 3.3 Construction of a Finite-State Lexicon ................ 88 3.4 Finite-State Transducers ........................ 91 3.4.1 Sequential Transducers and Determinism .......... 93 3.5 FSTs for Morphological Parsing ................... 94 3.6 Transducers and Orthographic Rules ................. 96 3.7 The Combination of an FST Lexicon and Rules ........... 99 3.8 Lexicon-Free FSTs: The Porter Stemmer............... 102 3.9 Word and Sentence Tokenization ................... 102 3.9.1 Segmentation in Chinese ................... 104 3.10 Detection and Correction of Spelling Errors ............. 106 3.11 Minimum Edit Distance ........................ 107 3.12 Human Morphological Processing .................. Ill 3.13 Summary ................................ 113 Bibliographical and Historical Notes ..................... 114 Exercises ................................... 115 JV-Grams 117 4.1 Word Counting in Corpora ...................... 119 4.2 Simple (Unsmoothed) N-Grams .................... 120 4.3 Training and Test Sets ......................... 125 4.3.1 iV-Gram Sensitivity to the Training Corpus ......... 126 4.3.2 Unknown Words: Open Versus Closed Vocabulary Tasks . . 129 4.4 Evaluating N-Grams: Perplexity ................... 129 4.5 Smoothing ............................... 131 4.5.1 Laplace Smoothing ...................... 132 4.5.2 Good-Turing Discounting .................. 135 4.5.3 Some Advanced Issues in Good-Turing Estimation ..... 136 4.6 Interpolation .............................. 138 4.7 Backoff ................................. 139 4.7.1 Advanced: Details of Computing Katz Backoff α and P* . . 141 4.8 Practical Issues: Toolkits and Data Formats .............. 142 4.9 Advanced Issues in Language Modeling ............... 143 4.9.1 Advanced Smoothing Methods: Kneser-Ney Smoothing . . 143 4.9.2 Class-Based tf-Grams.................... 145 4.9.3 Language Model Adaptation and Web Use ......... 146 Contents 11 4.9.4 Using Longer-Distance Information: A Brief Summary . . . 146 4.10 Advanced: Information Theory Background ............. 148 4.10.1 Cross-Entropy for Comparing Models ............ 150 4.11 Advanced: The Entropy of English and Entropy Rate Constancy . . 152 4.12 Summary ................................ 153 Bibliographical and Historical Notes ..................... 154 Exercises ................................... 155 Part-of-Speech Tagging 157 5.1 (Mostly) English Word Classes .................... 158 5.2 Tagsets for English ........................... 164 5.3 Part-of-Speech Tagging ........................ 167 5.4 Rule-Based Part-of-Speech Tagging .................. 169 5.5 HMM Part-of-Speech Tagging .................... 173 5.5.1 Computing the Most Likely Tag Sequence: An Example . . 176 5.5.2 Formalizing Hidden Markov Model Taggers ........ 178 5.5.3 Using the Viterbi Algorithm for HMM Tagging ....... 179 5.5.4 Extending the HMM Algorithm to Trigrams ......... 183 5.6 Transformation-Based Tagging .................... 185 5.6.1 How TBL Rules Are Applied ................ 186 5.6.2 How TBL Rules Are Learned ................ 186 5.7 Evaluation and Error Analysis ..................... 187 5.7.1 Error Analysis ........................ 190 5.8 Advanced Issues in Part-of-Speech Tagging ............. 191 5.8.1 Practical Issues: Tag Indeterminacy and Tokenization . . . . 191 5.8.2 Unknown Words ....................... 192 5.8.3 Part-of-Speech Tagging for Other Languages ........ 194 5.8.4 Tagger Combination ..................... 197 5.9 Advanced: The Noisy Channel Model for Spelling .......... 197 5.9.1 Contextual Spelling Error Correction ............ 201 5.10 Summary ................................ 202 Bibliographical and Historical Notes ..................... 203 Exercises ................................... 205 Hidden Markov and Maximum Entropy Models 207 6.1 Markov Chains ............................. 208 6.2 The Hidden Markov Model ...................... 210 6.3 Likelihood Computation: The Forward Algorithm .......... 213 6.4 Decoding: The Viterbi Algorithm ................... 218 6.5 HMM Training: The Forward-Backward Algorithm ......... 220 6.6 Maximum Entropy Models: Background ............... 227 6.6.1 Linear Regression ...................... 228 6.6.2 Logistic Regression ..................... 231 6.6.3 Logistic Regression: Classification ............. 233 6.6.4 Advanced: Learning in Logistic Regression ......... 234 6.7 Maximum Entropy Modeling ..................... 235 12 Contents 6.7.1 Why We Call It Maximum Entropy ............. 239 6.8 Maximum Entropy Markov Models .................. 241 6.8.1 Decoding and Learning in MEMMs ............. 244 6.9 Summary ................................ 245 Bibliographical and Historical Notes ..................... 246 Exercises ................................... 247 II Speech 7 Phonetics 249 7.1 Speech Sounds and Phonetic Transcription .............. 250 7.2 Articulatory Phonetics ......................... 251 7.2.1 The Vocal Organs ....................... 252 7.2.2 Consonants: Place of Articulation .............. 254 7.2.3 Consonants: Manner of Articulation ............. 255 7.2.4 Vowels ............................ 256 7.2.5 Syllables ........................... 257 7.3 Phonological Categories and Pronunciation Variation ........ 259 7.3.1 Phonetic Features ....................... 261 7.3.2 Predicting Phonetic Variation ................ 262 7.3.3 Factors Influencing Phonetic Variation ............ 263 7.4 Acoustic Phonetics and Signals .................... 264 7.4.1 Waves ............................. 264 7.4.2 Speech Sound Waves ..................... 265 7.4.3 Frequency and Amplitude; Pitch and Loudness ....... 267 7.4.4 Interpretation of Phones from a Waveform ......... 270 7.4.5 Spectra and the Frequency Domain ............. 270 7.4.6 The Source-Filter Model ................... 274 7.5 Phonetic Resources .......................... 275 7.6 Advanced. Articulatory and Gestural Phonology ........... 278 7.7 Summary ................................ 279 Bibliographical and Historical Notes ..................... 280 Exercises ................................... 281 8 Speech Synthesis 283 8.1 Text Normalization .......................... 285 8.1.1 Sentence Tokenization .................... 285 8.1.2 Non-Standard Words ..................... 286 8.1.3 Homograph Disambiguation ................. 290 8.2 Phonetic Analysis ........................... 291 8.2.1 Dictionary Lookup ...................... 291 8.2.2 Names ............................ 292 8.2.3 Grapheme-to-Phoneme Conversion ............. 293 8.3 Prosodie Analysis ........................... 296 8.3.1 Prosodie Structure ...................... 296 8.3.2 Prosodie Prominence ..................... 297 Contents 13 8.3.3 Tune ............................. 299 8.3.4 More Sophisticated Models: ToBI .............. 300 8.3.5 Computing Duration from Prosodie Labels ......... 302 8.3.6 Computing F0 from Prosodie Labels ............. 303 8.3.7 Final Result of Text Analysis: Internal Representation . . . 305 8.4 Diphone Waveform Synthesis ..................... 306 8.4.1 Steps for Building a Diphone Database ........... 306 8.4.2 Diphone Concatenation and TD-PSOLA for Prosody .... 308 8.5 Unit Selection (Waveform) Synthesis ................. 310 8.6 Evaluation ............................... 314 Bibliographical and Historical Notes ..................... 315 Exercises ................................... 318 9 Automatic Speech Recognition 319 9.1 Speech Recognition Architecture ................... 321 9.2 The Hidden Markov Model Applied to Speech ............ 325 9.3 Feature Extraction: MFCC Vectors .................. 329 9.3.1 Preemphasis ......................... 330 9.3.2 Windowing .......................... 330 9.3.3 Discrete Fourier Transform .................. 332 9.3.4 Mel Filter Bank and Log ................... 333 9.3.5 The Cepstram: Inverse Discrete Fourier Transform ..... 334 9.3.6 Deltas and Energy ...................... 336 9.3.7 Summary: MFCC ...................... 336 9.4 Acoustic Likelihood Computation ................... 337 9.4.1 Vector Quantization ..................... 337 9.4.2 Gaussian PDFs ........................ 340 9.4.3 Probabilities, Log-Probabilities, and Distance Functions . . 347 9.5 The Lexicon and Language Model .................. 348 9.6 Search and Decoding ......................... 348 9.7 Embedded Training .......................... 358 9.8 Evaluation: Word Error Rate ..................... 362 9.9 Summary ................................ 364 Bibliographical and Historical Notes ..................... 365 Exercises ................................... 367 10 Speech Recognition: Advanced Topics 369 10.1 Multipass Decoding: N-Best Lists and Lattices ............ 369 10.2 A* ( Stack ) Decoding ........................ 375 10.3 Context-Dependent Acoustic Models: Triphones ........... 379 10.4 Discriminative Training ........................ 383 10.4.1 Maximum Mutual Information Estimation .......... 384 10.4.2 Acoustic Models Based on Posterior Classifiers ....... 385 10.5 Modeling Variation .......................... 386 10.5.1 Environmental Variation and Noise ............. 386 10.5.2 Speaker Variation and Speaker Adaptation ......... 387 14 Contents 10.5.3 Pronunciation Modeling: Variation Due to Genre ...... 388 10.6 Metadata: Boundaries, Punctuation, and Disfluencies ........ 390 10.7 Speech Recognition by Humans .................... 392 10.8 Summary ................................ 393 Bibliographical and Historical Notes ..................... 393 Exercises ................................... 394 II Computational Phonology 395 11.1 Finite-State Phonology ........................ 395 11.2 Advanced Finite-State Phonology ................... 399 11.2.1 Harmony ........................... 399 11.2.2 Templatic Morphology .................... 400 11.3 Computational Optimality Theory ................... 401 11.3.1 Finite-State Transducer Models of Optimality Theory . ... 403 11.3.2 Stochastic Models of Optimality Theory ........... 404 11.4 Syllabification ............................. 406 11.5 Learning Phonology and Morphology ................. 409 11.5.1 Learning Phonological Rules ................. 409 11.5.2 Learning Morphology .................... 411 11.5.3 Learning in Optimality Theory ................ 414 11.6 Summary ................................ 415 Bibliographical and Historical Notes ..................... 415 Exercises ................................... 417 III Syntax 12 Formal Grammars of English 419 12.1 Constituency .............................. 420 12.2 Context-Free Grammars ........................ 421 12.2.1 Formal Definition of Context-Free Grammar ........ 425 12.3 Some Grammar Rules for English ................... 426 12.3.1 Sentence-Level Constructions ................ 426 12.3.2 Clauses and Sentences .................... 428 12.3.3 TheNounPhrase ....................... 428 12.3.4 Agreement .......................... 432 12.3.5 The Verb Phrase and Subcategorization ........... 434 12.3.6 Auxiliaries .......................... 436 12.3.7 Coordination ......................... 437 12.4 Treebanks ............................... 438 12.4.1 Example: The Penn Treebank Project ............ 438 12.4.2 Treebanks as Grammars ................... 440 12.4.3 Treebank Searching ..................... 442 12.4.4 Heads and Head Finding ................... 443 12.5 Grammar Equivalence and Normal Form ............... 446 12.6 Finite-State and Context-Free Grammars ............... 447 12.7 Dependency Grammars ........................ 448 Contents 15 12.7.1 The Relationship Between Dependencies and Heads .... 449 12.7.2 Categorial Grammar ..................... 451 12.8 Spoken Language Syntax ....................... 451 12.8.1 Disfluencies and Repair ................... 452 12.8.2 Treebanks for Spoken Language ............... 453 12.9 Grammars and Human Processing ................... 454 12.10 Summary ................................ 455 Bibliographical and Historical Notes ..................... 456 Exercises ................................... 458 13 Syntactic Parsing 461 13.1 Parsing as Search ........................... 462 13.1.1 Top-Down Parsing ...................... 463 13.1.2 Bottom-Up Parsing ...................... 464 13.1.3 Comparing Top-Down and Bottom-Up Parsing ....... 465 13.2 Ambiguity ............................... 466 13.3 Search in the Face of Ambiguity ................... 468 13.4 Dynamic Programming Parsing Methods ............... 469 13.4.1 CKY Parsing ......................... 470 13.4.2 The Earley Algorithm .................... 477 13.4.3 Chart Parsing ......................... 482 13.5 Partial Parsing ............................. 484 13.5.1 Finite-State Rule-Based Chunking .............. 486 13.5.2 Machine Learning-Based Approaches to Chunking ..... 486 13.5.3 Chunking-System Evaluations ................ 489 13.6 Summary ................................ 490 Bibliographical and Historical Notes ..................... 491 Exercises ................................... 492 14 Statistical Parsing 493 14.1 Probabilistic Context-Free Grammars ................. 494 14.1.1 PCFGs for Disambiguation .................. 495 14.1.2 PCFGs for Language Modeling ............... 497 14.2 Probabilistic CKY Parsing of PCFGs ................. 498 14.3 Ways to Learn PCFG Rule Probabilities ............... 501 14.4 Problems with PCFGs ......................... 502 14.4.1 Independence Assumptions Miss Structural Dependencies Be¬ tween Rules .......................... 502 14.4.2 Lack of Sensitivity to Lexical Dependencies ........ 503 14.5 Improving PCFGs by Splitting Non-Terminals ............ 505 14.6 Probabilistic Lexicalized CFGs .................... 507 14.6.1 The Collins Parser ...................... 509 14.6.2 Advanced: Further Details of the Coffins Parser ....... 511 14.7 Evaluating Parsers ........................... 513 14.8 Advanced: Discriminative Reranking ................. 515 14.9 Advanced: Parser-Based Language Modeling ............. 516 16 Contents 14.10 Human Parsing ............................. 517 14.11 Summary ................................ 519 Bibliographical and Historical Notes..................... 520 Exercises ................................... 522 15 Features and Unification 523 15.1 Feature Structures ........................... 524 15.2 Unification of Feature Structures ................... 526 15.3 Feature Structures in the Grammar .................. 531 15.3.1 Agreement .......................... 532 15.3.2 Head Features ........................ 534 15.3.3 Subcategorization ...................... 535 15.3.4 Long-Distance Dependencies ................ 540 15.4 Implementation of Unification .................... 541 15.4.1 Unification Data Structures .................. 541 15.4.2 The Unification Algorithm .................. 543 15.5 Parsing with Unification Constraints ................. 547 15.5.1 Integration of Unification into an Earley Parser ....... 548 15.5.2 Unification-Based Parsing .................. 553 15.6 Types and Inheritance ......................... 555 15.6.1 Advanced: Extensions to Typing .............. 558 15.6.2 Other Extensions to Unification ............... 559 15.7 Summary ................................ 559 Bibliographical and Historical Notes ..................... 560 Exercises ................................... 561 16 Language and Complexity 563 16.1 The Chomsky Hierarchy ........................ 564 16.2 Ways to Tell if a Language Isn t Regular ............... 566 16.2.1 The Pumping Lemma .................... 567 16.2.2 Proofs that Various Natural Languages Are Not Regular . . 569 16.3 Is Natural Language Context Free? .................. 571 16.4 Complexity and Human Processing .................. 573 16.5 Summary ................................ 576 Bibliographical and Historical Notes ..................... 577 Exercises ................................... 578 IV Semantics and Pragmatics 17 The Representation of Meaning 579 17.1 Computational Desiderata for Representations ............ 581 17.1.1 Verifiability .......................... 581 17.1.2 Unambiguous Representations ................ 582 17.1.3 Canonical Form ....................... 583 17.1.4 Inference and Variables .................... 584 17.1.5 Expressiveness ........................ 585 Contents 17 17.2 Model-Theoretic Semantics ...................... 586 17.3 First-Order Logic ........................... 589 17.3.1 Basic Elements of First-Order Logic ............. 589 17.3.2 Variables and Quantifiers ................... 591 17.3.3 Lambda Notation ....................... 593 17.3.4 The Semantics of First-Order Logic ............. 594 17.3.5 Inference ........................... 595 17.4 Event and State Representations .................... 597 17.4.1 Representing Time ...................... 600 17.4.2 Aspect ............................ 603 17.5 Description Logics ........................... 606 17.6 Embodied and Situated Approaches to Meaning ........... 612 17.7 Summary ................................ 614 Bibliographical and Historical Notes ..................... 614 Exercises ................................... 616 18 Computational Semantics 617 18.1 Syntax-Driven Semantic Analysis ................... 617 18.2 Semantic Augmentations to Syntactic Rules ............. 619 18.3 Quantifier Scope Ambiguity and Underspecification ......... 626 18.3.1 Store and Retrieve Approaches ................ 626 18.3.2 Constraint-Based Approaches ................ 629 18.4 Unification-Based Approaches to Semantic Analysis ......... 632 18.5 Integration of Semantics into the Earley Parser ............ 638 18.6 Idioms and Compositionality ..................... 639 18.7 Summary ................................ 641 Bibliographical and Historical Notes ..................... 641 Exercises ................................... 643 19 Lexical Semantics 645 19.1 Word Senses .............................. 646 19.2 Relations Between Senses ....................... 649 19.2.1 Synonymy and Antonymy .................. 649 19.2.2 Hyponymy .......................... 650 19.2.3 Semantic Fields ........................ 651 19.3 WordNet: A Database of Lexical Relations .............. 651 19.4 Event Participants ........................... 653 19.4.1 Thematic Roles ........................ 654 19.4.2 Diathesis Alternations .................... 656 19.4.3 Problems with Thematic Roles ................ 657 19.4.4 The Proposition Bank .................... 658 19.4.5 FrameNet ........................... 659 19.4.6 Selecţionai Restrictions ................... 661 19.5 Primitive Decomposition ....................... 663 19.6 Advanced: Metaphor ......................... 665 19.7 Summary ................................ 666 18 Contents Bibliographical and Historical Notes ..................... 667 Exercises ................................... 668 20 Computational Lexical Semantics 671 20.1 Word Sense Disambiguation: Overview ................ 672 20.2 Supervised Word Sense Disambiguation ............... 673 20.2.1 Feature Extraction for Supervised Learning ......... 674 20.2.2 Naive Bayes and Decision List Classifiers .......... 675 20.3 WSD Evaluation, Baselines, and Ceilings ............... 678 20.4 WSD: Dictionary and Thesaurus Methods .............. 680 20.4.1 The Lesk Algorithm ..................... 680 20.4.2 Selecţionai Restrictions and Selecţionai Preferences .... 682 20.5 Minimally Supervised WSD: Bootstrapping ............. 684 20.6 Word Similarity: Thesaurus Methods ................. 686 20.7 Word Similarity: Distributional Methods ............... 692 20.7.1 Defining a Word s Co-Occurrence Vectors .......... 693 20.7.2 Measuring Association with Context ............. 695 20.7.3 Defining Similarity Between Two Vectors .......... 697 20.7.4 Evaluating Distributional Word Similarity .......... 701 20.8 Hyponymy and Other Word Relations ................. 701 20.9 Semantic Role Labeling ........................ 704 20.10 Advanced: Unsupervised Sense Disambiguation ........... 708 20.11 Summary ................................ 709 Bibliographical and Historical Notes ..................... 710 Exercises ................................... 713 21 Computational Discourse 715 21.1 Discourse Segmentation ........................ 718 21.1.1 Unsupervised Discourse Segmentation ........... 718 21.1.2 Supervised Discourse Segmentation ............. 720 21.1.3 Discourse Segmentation Evaluation ............. 722 21.2 Text Coherence ............................ 723 21.2.1 Rhetorical Structure Theory ................. 724 21.2.2 Automatic Coherence Assignment .............. 726 21.3 Reference Resolution ......................... 729 21.4 Reference Phenomena ......................... 732 21.4.1 Five Types of Referring Expressions ............. 732 21.4.2 Information Status ...................... 734 21.5 Features for Pronominal Anaphora Resolution ............ 735 21.5.1 Features for Filtering Potential Referents .......... 735 21.5.2 Preferences in Pronoun Interpretation ............ 736 21.6 Three Algorithms for Anaphora Resolution .............. 738 21.6.1 Pronominal Anaphora Baseline: The Hobbs Algorithm .. 738 21.6.2 A Centering Algorithm for Anaphora Resolution ...... 740 21.6.3 A Log-Linear Model for Pronominal Anaphora Resolution . 742 21.6.4 Features for Pronominal Anaphora Resolution ....... 743 Contents 19 21.7 Coreference Resolution........................ 744 21.8 Evaluation of Coreference Resolution................. 74.6 21.9 Advanced: Inference-Based Coherence Resolution.......... 747 21.10 Psycholinguistic Studies of Reference................. 752 21.11 Summary ................................ 753 Bibliographical and Historical Notes ..................... 754 Exercises ................................... 756 V Applications 22 Information Extraction 759 22.1 Named Entity Recognition ...................... 761 22.1.1 Ambiguity in Named Entity Recognition .......... 763 22.1.2 NER as Sequence Labeling ................. 763 22.1.3 Evaluation of Named Entity Recognition .......... 766 22.1.4 Practical NER Architectures ................. 768 22.2 Relation Detection and Classification ................. 768 22.2.1 Supervised Learning Approaches to Relation Analysis . . . 769 22.2.2 Lightly Supervised Approaches to Relation Analysis .... 772 22.2.3 Evaluation of Relation Analysis Systems .......... 776 22.3 Temporal and Event Processing .................... 777 22.3.1 Temporal Expression Recognition .............. 777 22.3.2 Temporal Normalization ................... 780 22.3.3 Event Detection and Analysis ................ 783 22.3.4 TimeBank ........................... 784 22.4 Template Filling ............................ 786 22.4.1 Statistical Approaches to Template-Filling ......... 786 22.4.2 Finite-State Template-Filling Systems ............ 788 22.5 Advanced: Biomedical Information Extraction ............ 791 22.5.1 Biological Named Entity Recognition ............ 792 22.5.2 Gene Normalization ..................... 793 22.5.3 Biological Roles and Relations ................ 794 22.6 Summary ................................ 796 Bibliographical and Historical Notes ..................... 796 Exercises ................................... 797 23 Question Answering and Summarization 799 23.1 Information Retrieval ......................... 801 23.1.1 The Vector Space Model ................... 802 23.1.2 Term Weighting ....................... 804 23.1.3 Term Selection and Creation ................. 806 23.1.4 Evaluation of Information-Retrieval Systems ........ 806 23.1.5 Homonymy, Polysemy, and Synonymy ........... 810 23.1.6 Ways to Improve User Queries ................ 810 23.2 Factoid Question Answering ..................... 812 23.2.1 Question Processing ..................... 813 20 Contents 23.2.2 Passage Retrieval ....................... 815 23.2.3 Answer Processing...................... 817 23.2.4 Evaluation of Factoid Answers ................ 821 23.3 Summarization ............................. 821 23.4 Single-Document Summarization ................... 824 23.4.1 Unsupervised Content Selection ............... 824 23.4.2 Unsupervised Summarization Based on Rhetorical Parsing . 826 23.4.3 Supervised Content Selection ................ 828 23.4.4 Sentence Simplification ................... 829 23.5 Multi-Document Summarization ................... 830 23.5.1 Content Selection in Multi-Document Summarization ... 831 23.5.2 Information Ordering in Multi-Document Summarization . 832 23.6 Focused Summarization and Question Answering .......... 835 23.7 Summarization Evaluation ....................... 839 23.8 Summary ................................ 841 Bibliographical and Historical Notes ..................... 842 Exercises ................................... 844 24 Dialogue and Conversational Agents 847 24.1 Properties of Human Conversations .................. 849 24.1.1 Turns and Turn-Taking .................... 849 24.1.2 Language as Action: Speech Acts .............. 851 24.1.3 Language as Joint Action: Grounding ............ 852 24.1.4 Conversational Structure ................... 854 24.1.5 Conversational Implicature .................. 855 24.2 Basic Dialogue Systems ........................ 857 24.2.1 ASR Component ....................... 857 24.2.2 NLU Component ....................... 858 24.2.3 Generation and TTS Components .............. 861 24.2.4 Dialogue Manager ...................... 863 24.2.5 Dealing with Errors: Confirmation and Rejection ...... 867 24.3 VoiceXML ............................... 868 24.4 Dialogue System Design and Evaluation ............... 872 24.4.1 Designing Dialogue Systems ................. 872 24.4.2 Evaluating Dialogue Systems ................ 872 24.5 Information-State and Dialogue Acts ................. 874 24.5.1 Using Dialogue Acts ..................... 876 24.5.2 Interpreting Dialogue Acts .................. 877 24.5.3 Detecting Correction Acts .................. 880 24.5.4 Generating Dialogue Acts: Confirmation and Rejection ... 881 24.6 Markov Decision Process Architecture ................ 882 24.7 Advanced: Plan-Based Dialogue Agents ............... 886 24.7.1 Plan-Inferential Interpretation and Production ........ 887 24.7.2 The Intentional Structure of Dialogue ............ 889 24.8 Summary ................................ g9l Bibliographical and Historical Notes ..................... 892 Contents 21 Exercises ................................... 894 25 Machine Translation 895 25.1 Why Machine Translation Is Hard ................... 898 25.1.1 Typology ........................... 898 25.1.2 Other Structural Divergences ................. 900 25.1.3 Lexical Divergences ..................... 901 25.2 Classical MT and the Vauquois Triangle ............... 903 25.2.1 Direct Translation ...................... 904 25.2.2 Transfer ............................ 906 25.2.3 Combined Direct and Transfer Approaches in Classic MT . 908 25.2.4 The Interlingua Idea: Using Meaning ............ 909 25.3 Statistical MT ............................. 910 25.4 P(F E): The Phrase-Based Translation Model ............ 913 25.5 Alignment in MT ........................... 915 25.5.1 IBM Model 1......................... 916 25.5.2 HMM Alignment ....................... 919 25.6 Training Alignment Models ...................... 921 25.6.1 EM for Training Alignment Models ............. 922 25.7 Symmetrizing Alignments for Phrase-Based MT ........... 924 25.8 Decoding for Phrase-Based Statistical MT .............. 926 25.9 MT Evaluation ............................. 930 25.9.1 Using Human Raters ..................... 930 25.9.2 Automatic Evaluation: BLEU ................ 931 25.10 Advanced: Syntactic Models for MT ................. 934 25.11 Advanced: IBM Model 3 and Fertility ................ 935 25.11.1 Training for Model 3..................... 939 25.12 Advanced: Log-Linear Models for MT ................ 939 25.13 Summary ................................ 940 Bibliographical and Historical Notes ..................... 941 Exercises ................................... 943 Bibliography 945 Author Index 995 Subject Index 1007
adam_txt	Contents Foreword 23 Preface 25 About the Authors 31 1 Introduction 35 1.1 Knowledge in Speech and Language Processing . 36 1.2 Ambiguity . 38 1.3 Models and Algorithms . 39 1.4 Language, Thought, and Understanding . 40 1.5 The State of the Art . 42 1.6 Some Brief History . 43 1.6.1 Foundational Insights: 1940s and 1950s. 43 1.6.2 The Two Camps: 1957-1970. 44 1.6.3 Four Paradigms: 1970-1983. 45 1.6.4 Empiricism and Finite-State Models Redux: 1983-1993 . 46 1.6.5 The Field Comes Together: 1994-1999. 46 1.6.6 The Rise of Machine Learning: 2000-2008. 46 1.6.7 On Multiple Discoveries . 47 1.6.8 A Final Brief Note on Psychology . 48 1.7 Summary . 48 Bibliographical and Historical Notes . 49 1 Words 2 Regular Expressions and Automata 51 2.1 Regular Expressions . 51 2.1.1 Basic Regular Expression Patterns . 52 2.1.2 Disjunction, Grouping, and Precedence . 55 2.1.3 A Simple Example . 56 2.1.4 A More Complex Example . 57 2.1.5 Advanced Operators . 58 2.1.6 Regular Expression Substitution, Memory, and ELIZA . 59 2.2 Finite-State Automata . 60 2.2.1 Use of an FSA to Recognize Sheeptalk . 61 2.2.2 Formal Languages . 64 2.2.3 Another Example . 65 2.2.4 Non-Deterministic FSAs . 66 2.2.5 Use of an NFSA to Accept Strings . 67 2.2.6 Recognition as Search . 69 2.2.7 Relation of Deterministic and Non-Deterministic Automata 72 2.3 Regular Languages and FSAs . 72 2.4 Summary . 75 10 Contents Bibliographical and Historical Notes . 76 Exercises . 76 Words and Transducers 79 3.1 Survey of (Mostly) English Morphology . 81 3.1.1 Inflectional Morphology . 82 3.1.2 Derivational Morphology . 84 3.1.3 Cliticization . 85 3.1.4 Non-Concatenative Morphology . 85 3.1.5 Agreement . 86 3.2 Finite-State Morphological Parsing . 86 3.3 Construction of a Finite-State Lexicon . 88 3.4 Finite-State Transducers . 91 3.4.1 Sequential Transducers and Determinism . 93 3.5 FSTs for Morphological Parsing . 94 3.6 Transducers and Orthographic Rules . 96 3.7 The Combination of an FST Lexicon and Rules . 99 3.8 Lexicon-Free FSTs: The Porter Stemmer. 102 3.9 Word and Sentence Tokenization . 102 3.9.1 Segmentation in Chinese . 104 3.10 Detection and Correction of Spelling Errors . 106 3.11 Minimum Edit Distance . 107 3.12 Human Morphological Processing . Ill 3.13 Summary . 113 Bibliographical and Historical Notes . 114 Exercises . 115 JV-Grams 117 4.1 Word Counting in Corpora . 119 4.2 Simple (Unsmoothed) N-Grams . 120 4.3 Training and Test Sets . 125 4.3.1 iV-Gram Sensitivity to the Training Corpus . 126 4.3.2 Unknown Words: Open Versus Closed Vocabulary Tasks . . 129 4.4 Evaluating N-Grams: Perplexity . 129 4.5 Smoothing . 131 4.5.1 Laplace Smoothing . 132 4.5.2 Good-Turing Discounting . 135 4.5.3 Some Advanced Issues in Good-Turing Estimation . 136 4.6 Interpolation . 138 4.7 Backoff . 139 4.7.1 Advanced: Details of Computing Katz Backoff α and P* . . 141 4.8 Practical Issues: Toolkits and Data Formats . 142 4.9 Advanced Issues in Language Modeling . 143 4.9.1 Advanced Smoothing Methods: Kneser-Ney Smoothing . . 143 4.9.2 Class-Based tf-Grams. 145 4.9.3 Language Model Adaptation and Web Use . 146 Contents 11 4.9.4 Using Longer-Distance Information: A Brief Summary . . . 146 4.10 Advanced: Information Theory Background . 148 4.10.1 Cross-Entropy for Comparing Models . 150 4.11 Advanced: The Entropy of English and Entropy Rate Constancy . . 152 4.12 Summary . 153 Bibliographical and Historical Notes . 154 Exercises . 155 Part-of-Speech Tagging 157 5.1 (Mostly) English Word Classes . 158 5.2 Tagsets for English . 164 5.3 Part-of-Speech Tagging . 167 5.4 Rule-Based Part-of-Speech Tagging . 169 5.5 HMM Part-of-Speech Tagging . 173 5.5.1 Computing the Most Likely Tag Sequence: An Example . . 176 5.5.2 Formalizing Hidden Markov Model Taggers . 178 5.5.3 Using the Viterbi Algorithm for HMM Tagging . 179 5.5.4 Extending the HMM Algorithm to Trigrams . 183 5.6 Transformation-Based Tagging . 185 5.6.1 How TBL Rules Are Applied . 186 5.6.2 How TBL Rules Are Learned . 186 5.7 Evaluation and Error Analysis . 187 5.7.1 Error Analysis . 190 5.8 Advanced Issues in Part-of-Speech Tagging . 191 5.8.1 Practical Issues: Tag Indeterminacy and Tokenization . . . . 191 5.8.2 Unknown Words . 192 5.8.3 Part-of-Speech Tagging for Other Languages . 194 5.8.4 Tagger Combination . 197 5.9 Advanced: The Noisy Channel Model for Spelling . 197 5.9.1 Contextual Spelling Error Correction . 201 5.10 Summary . 202 Bibliographical and Historical Notes . 203 Exercises . 205 Hidden Markov and Maximum Entropy Models 207 6.1 Markov Chains . 208 6.2 The Hidden Markov Model . 210 6.3 Likelihood Computation: The Forward Algorithm . 213 6.4 Decoding: The Viterbi Algorithm . 218 6.5 HMM Training: The Forward-Backward Algorithm . 220 6.6 Maximum Entropy Models: Background . 227 6.6.1 Linear Regression . 228 6.6.2 Logistic Regression . 231 6.6.3 Logistic Regression: Classification . 233 6.6.4 Advanced: Learning in Logistic Regression . 234 6.7 Maximum Entropy Modeling . 235 12 Contents 6.7.1 Why We Call It Maximum Entropy . 239 6.8 Maximum Entropy Markov Models . 241 6.8.1 Decoding and Learning in MEMMs . 244 6.9 Summary . 245 Bibliographical and Historical Notes . 246 Exercises . 247 II Speech 7 Phonetics 249 7.1 Speech Sounds and Phonetic Transcription . 250 7.2 Articulatory Phonetics . 251 7.2.1 The Vocal Organs . 252 7.2.2 Consonants: Place of Articulation . 254 7.2.3 Consonants: Manner of Articulation . 255 7.2.4 Vowels . 256 7.2.5 Syllables . 257 7.3 Phonological Categories and Pronunciation Variation . 259 7.3.1 Phonetic Features . 261 7.3.2 Predicting Phonetic Variation . 262 7.3.3 Factors Influencing Phonetic Variation . 263 7.4 Acoustic Phonetics and Signals . 264 7.4.1 Waves . 264 7.4.2 Speech Sound Waves . 265 7.4.3 Frequency and Amplitude; Pitch and Loudness . 267 7.4.4 Interpretation of Phones from a Waveform . 270 7.4.5 Spectra and the Frequency Domain . 270 7.4.6 The Source-Filter Model . 274 7.5 Phonetic Resources . 275 7.6 Advanced." Articulatory and Gestural Phonology . 278 7.7 Summary . 279 Bibliographical and Historical Notes . 280 Exercises . 281 8 Speech Synthesis 283 8.1 Text Normalization . 285 8.1.1 Sentence Tokenization . 285 8.1.2 Non-Standard Words . 286 8.1.3 Homograph Disambiguation . 290 8.2 Phonetic Analysis . 291 8.2.1 Dictionary Lookup . 291 8.2.2 Names . 292 8.2.3 Grapheme-to-Phoneme Conversion . 293 8.3 Prosodie Analysis . 296 8.3.1 Prosodie Structure . 296 8.3.2 Prosodie Prominence . 297 Contents 13 8.3.3 Tune . 299 8.3.4 More Sophisticated Models: ToBI . 300 8.3.5 Computing Duration from Prosodie Labels . 302 8.3.6 Computing F0 from Prosodie Labels . 303 8.3.7 Final Result of Text Analysis: Internal Representation . . . 305 8.4 Diphone Waveform Synthesis . 306 8.4.1 Steps for Building a Diphone Database . 306 8.4.2 Diphone Concatenation and TD-PSOLA for Prosody . 308 8.5 Unit Selection (Waveform) Synthesis . 310 8.6 Evaluation . 314 Bibliographical and Historical Notes . 315 Exercises . 318 9 Automatic Speech Recognition 319 9.1 Speech Recognition Architecture . 321 9.2 The Hidden Markov Model Applied to Speech . 325 9.3 Feature Extraction: MFCC Vectors . 329 9.3.1 Preemphasis . 330 9.3.2 Windowing . 330 9.3.3 Discrete Fourier Transform . 332 9.3.4 Mel Filter Bank and Log . 333 9.3.5 The Cepstram: Inverse Discrete Fourier Transform . 334 9.3.6 Deltas and Energy . 336 9.3.7 Summary: MFCC . 336 9.4 Acoustic Likelihood Computation . 337 9.4.1 Vector Quantization . 337 9.4.2 Gaussian PDFs . 340 9.4.3 Probabilities, Log-Probabilities, and Distance Functions . . 347 9.5 The Lexicon and Language Model . 348 9.6 Search and Decoding . 348 9.7 Embedded Training . 358 9.8 Evaluation: Word Error Rate . 362 9.9 Summary . 364 Bibliographical and Historical Notes . 365 Exercises . 367 10 Speech Recognition: Advanced Topics 369 10.1 Multipass Decoding: N-Best Lists and Lattices . 369 10.2 A* ("Stack") Decoding . 375 10.3 Context-Dependent Acoustic Models: Triphones . 379 10.4 Discriminative Training . 383 10.4.1 Maximum Mutual Information Estimation . 384 10.4.2 Acoustic Models Based on Posterior Classifiers . 385 10.5 Modeling Variation . 386 10.5.1 Environmental Variation and Noise . 386 10.5.2 Speaker Variation and Speaker Adaptation . 387 14 Contents 10.5.3 Pronunciation Modeling: Variation Due to Genre . 388 10.6 Metadata: Boundaries, Punctuation, and Disfluencies . 390 10.7 Speech Recognition by Humans . 392 10.8 Summary . 393 Bibliographical and Historical Notes . 393 Exercises . 394 II Computational Phonology 395 11.1 Finite-State Phonology . 395 11.2 Advanced Finite-State Phonology . 399 11.2.1 Harmony . 399 11.2.2 Templatic Morphology . 400 11.3 Computational Optimality Theory . 401 11.3.1 Finite-State Transducer Models of Optimality Theory . . 403 11.3.2 Stochastic Models of Optimality Theory . 404 11.4 Syllabification . 406 11.5 Learning Phonology and Morphology . 409 11.5.1 Learning Phonological Rules . 409 11.5.2 Learning Morphology . 411 11.5.3 Learning in Optimality Theory . 414 11.6 Summary . 415 Bibliographical and Historical Notes . 415 Exercises . 417 III Syntax 12 Formal Grammars of English 419 12.1 Constituency . 420 12.2 Context-Free Grammars . 421 12.2.1 Formal Definition of Context-Free Grammar . 425 12.3 Some Grammar Rules for English . 426 12.3.1 Sentence-Level Constructions . 426 12.3.2 Clauses and Sentences . 428 12.3.3 TheNounPhrase . 428 12.3.4 Agreement . 432 12.3.5 The Verb Phrase and Subcategorization . 434 12.3.6 Auxiliaries . 436 12.3.7 Coordination . 437 12.4 Treebanks . 438 12.4.1 Example: The Penn Treebank Project . 438 12.4.2 Treebanks as Grammars . 440 12.4.3 Treebank Searching . 442 12.4.4 Heads and Head Finding . 443 12.5 Grammar Equivalence and Normal Form . 446 12.6 Finite-State and Context-Free Grammars . 447 12.7 Dependency Grammars . 448 Contents 15 12.7.1 The Relationship Between Dependencies and Heads . 449 12.7.2 Categorial Grammar . 451 12.8 Spoken Language Syntax . 451 12.8.1 Disfluencies and Repair . 452 12.8.2 Treebanks for Spoken Language . 453 12.9 Grammars and Human Processing . 454 12.10 Summary . 455 Bibliographical and Historical Notes . 456 Exercises . 458 13 Syntactic Parsing 461 13.1 Parsing as Search . 462 13.1.1 Top-Down Parsing . 463 13.1.2 Bottom-Up Parsing . 464 13.1.3 Comparing Top-Down and Bottom-Up Parsing . 465 13.2 Ambiguity . 466 13.3 Search in the Face of Ambiguity . 468 13.4 Dynamic Programming Parsing Methods . 469 13.4.1 CKY Parsing . 470 13.4.2 The Earley Algorithm . 477 13.4.3 Chart Parsing . 482 13.5 Partial Parsing . 484 13.5.1 Finite-State Rule-Based Chunking . 486 13.5.2 Machine Learning-Based Approaches to Chunking . 486 13.5.3 Chunking-System Evaluations . 489 13.6 Summary . 490 Bibliographical and Historical Notes . 491 Exercises . 492 14 Statistical Parsing 493 14.1 Probabilistic Context-Free Grammars . 494 14.1.1 PCFGs for Disambiguation . 495 14.1.2 PCFGs for Language Modeling . 497 14.2 Probabilistic CKY Parsing of PCFGs . 498 14.3 Ways to Learn PCFG Rule Probabilities . 501 14.4 Problems with PCFGs . 502 14.4.1 Independence Assumptions Miss Structural Dependencies Be¬ tween Rules . 502 14.4.2 Lack of Sensitivity to Lexical Dependencies . 503 14.5 Improving PCFGs by Splitting Non-Terminals . 505 14.6 Probabilistic Lexicalized CFGs . 507 14.6.1 The Collins Parser . 509 14.6.2 Advanced: Further Details of the Coffins Parser . 511 14.7 Evaluating Parsers . 513 14.8 Advanced: Discriminative Reranking . 515 14.9 Advanced: Parser-Based Language Modeling . 516 16 Contents 14.10 Human Parsing . 517 14.11 Summary . 519 Bibliographical and Historical Notes. 520 Exercises . 522 15 Features and Unification 523 15.1 Feature Structures . 524 15.2 Unification of Feature Structures . 526 15.3 Feature Structures in the Grammar . 531 15.3.1 Agreement . 532 15.3.2 Head Features . 534 15.3.3 Subcategorization . 535 15.3.4 Long-Distance Dependencies . 540 15.4 Implementation of Unification . 541 15.4.1 Unification Data Structures . 541 15.4.2 The Unification Algorithm . 543 15.5 Parsing with Unification Constraints . 547 15.5.1 Integration of Unification into an Earley Parser . 548 15.5.2 Unification-Based Parsing . 553 15.6 Types and Inheritance . 555 15.6.1 Advanced: Extensions to Typing . 558 15.6.2 Other Extensions to Unification . 559 15.7 Summary . 559 Bibliographical and Historical Notes . 560 Exercises . 561 16 Language and Complexity 563 16.1 The Chomsky Hierarchy . 564 16.2 Ways to Tell if a Language Isn't Regular . 566 16.2.1 The Pumping Lemma . 567 16.2.2 Proofs that Various Natural Languages Are Not Regular . . 569 16.3 Is Natural Language Context Free? . 571 16.4 Complexity and Human Processing . 573 16.5 Summary . 576 Bibliographical and Historical Notes . 577 Exercises . 578 IV Semantics and Pragmatics 17 The Representation of Meaning 579 17.1 Computational Desiderata for Representations . 581 17.1.1 Verifiability . 581 17.1.2 Unambiguous Representations . 582 17.1.3 Canonical Form . 583 17.1.4 Inference and Variables . 584 17.1.5 Expressiveness . 585 Contents 17 17.2 Model-Theoretic Semantics . 586 17.3 First-Order Logic . 589 17.3.1 Basic Elements of First-Order Logic . 589 17.3.2 Variables and Quantifiers . 591 17.3.3 Lambda Notation . 593 17.3.4 The Semantics of First-Order Logic . 594 17.3.5 Inference . 595 17.4 Event and State Representations . 597 17.4.1 Representing Time . 600 17.4.2 Aspect . 603 17.5 Description Logics . 606 17.6 Embodied and Situated Approaches to Meaning . 612 17.7 Summary . 614 Bibliographical and Historical Notes . 614 Exercises . 616 18 Computational Semantics 617 18.1 Syntax-Driven Semantic Analysis . 617 18.2 Semantic Augmentations to Syntactic Rules . 619 18.3 Quantifier Scope Ambiguity and Underspecification . 626 18.3.1 Store and Retrieve Approaches . 626 18.3.2 Constraint-Based Approaches . 629 18.4 Unification-Based Approaches to Semantic Analysis . 632 18.5 Integration of Semantics into the Earley Parser . 638 18.6 Idioms and Compositionality . 639 18.7 Summary . 641 Bibliographical and Historical Notes . 641 Exercises . 643 19 Lexical Semantics 645 19.1 Word Senses . 646 19.2 Relations Between Senses . 649 19.2.1 Synonymy and Antonymy . 649 19.2.2 Hyponymy . 650 19.2.3 Semantic Fields . 651 19.3 WordNet: A Database of Lexical Relations . 651 19.4 Event Participants . 653 19.4.1 Thematic Roles . 654 19.4.2 Diathesis Alternations . 656 19.4.3 Problems with Thematic Roles . 657 19.4.4 The Proposition Bank . 658 19.4.5 FrameNet . 659 19.4.6 Selecţionai Restrictions . 661 19.5 Primitive Decomposition . 663 19.6 Advanced: Metaphor . 665 19.7 Summary . 666 18 Contents Bibliographical and Historical Notes . 667 Exercises . 668 20 Computational Lexical Semantics 671 20.1 Word Sense Disambiguation: Overview . 672 20.2 Supervised Word Sense Disambiguation . 673 20.2.1 Feature Extraction for Supervised Learning . 674 20.2.2 Naive Bayes and Decision List Classifiers . 675 20.3 WSD Evaluation, Baselines, and Ceilings . 678 20.4 WSD: Dictionary and Thesaurus Methods . 680 20.4.1 The Lesk Algorithm . 680 20.4.2 Selecţionai Restrictions and Selecţionai Preferences . 682 20.5 Minimally Supervised WSD: Bootstrapping . 684 20.6 Word Similarity: Thesaurus Methods . 686 20.7 Word Similarity: Distributional Methods . 692 20.7.1 Defining a Word's Co-Occurrence Vectors . 693 20.7.2 Measuring Association with Context . 695 20.7.3 Defining Similarity Between Two Vectors . 697 20.7.4 Evaluating Distributional Word Similarity . 701 20.8 Hyponymy and Other Word Relations . 701 20.9 Semantic Role Labeling . 704 20.10 Advanced: Unsupervised Sense Disambiguation . 708 20.11 Summary . 709 Bibliographical and Historical Notes . 710 Exercises . 713 21 Computational Discourse 715 21.1 Discourse Segmentation . 718 21.1.1 Unsupervised Discourse Segmentation . 718 21.1.2 Supervised Discourse Segmentation . 720 21.1.3 Discourse Segmentation Evaluation . 722 21.2 Text Coherence . 723 21.2.1 Rhetorical Structure Theory . 724 21.2.2 Automatic Coherence Assignment . 726 21.3 Reference Resolution . 729 21.4 Reference Phenomena . 732 21.4.1 Five Types of Referring Expressions . 732 21.4.2 Information Status . 734 21.5 Features for Pronominal Anaphora Resolution . 735 21.5.1 Features for Filtering Potential Referents . 735 21.5.2 Preferences in Pronoun Interpretation . 736 21.6 Three Algorithms for Anaphora Resolution . 738 21.6.1 Pronominal Anaphora Baseline: The Hobbs Algorithm . 738 21.6.2 A Centering Algorithm for Anaphora Resolution . 740 21.6.3 A Log-Linear Model for Pronominal Anaphora Resolution . 742 21.6.4 Features for Pronominal Anaphora Resolution . 743 Contents 19 21.7 Coreference Resolution. 744 21.8 Evaluation of Coreference Resolution. 74.6 21.9 Advanced: Inference-Based Coherence Resolution. 747 21.10 Psycholinguistic Studies of Reference. 752 21.11 Summary . 753 Bibliographical and Historical Notes . 754 Exercises . 756 V Applications 22 Information Extraction 759 22.1 Named Entity Recognition . 761 22.1.1 Ambiguity in Named Entity Recognition . 763 22.1.2 NER as Sequence Labeling . 763 22.1.3 Evaluation of Named Entity Recognition . 766 22.1.4 Practical NER Architectures . 768 22.2 Relation Detection and Classification . 768 22.2.1 Supervised Learning Approaches to Relation Analysis . . . 769 22.2.2 Lightly Supervised Approaches to Relation Analysis . 772 22.2.3 Evaluation of Relation Analysis Systems . 776 22.3 Temporal and Event Processing . 777 22.3.1 Temporal Expression Recognition . 777 22.3.2 Temporal Normalization . 780 22.3.3 Event Detection and Analysis . 783 22.3.4 TimeBank . 784 22.4 Template Filling . 786 22.4.1 Statistical Approaches to Template-Filling . 786 22.4.2 Finite-State Template-Filling Systems . 788 22.5 Advanced: Biomedical Information Extraction . 791 22.5.1 Biological Named Entity Recognition . 792 22.5.2 Gene Normalization . 793 22.5.3 Biological Roles and Relations . 794 22.6 Summary . 796 Bibliographical and Historical Notes . 796 Exercises . 797 23 Question Answering and Summarization 799 23.1 Information Retrieval . 801 23.1.1 The Vector Space Model . 802 23.1.2 Term Weighting . 804 23.1.3 Term Selection and Creation . 806 23.1.4 Evaluation of Information-Retrieval Systems . 806 23.1.5 Homonymy, Polysemy, and Synonymy . 810 23.1.6 Ways to Improve User Queries . 810 23.2 Factoid Question Answering . 812 23.2.1 Question Processing . 813 20 Contents 23.2.2 Passage Retrieval . 815 23.2.3 Answer Processing. 817 23.2.4 Evaluation of Factoid Answers . 821 23.3 Summarization . 821 23.4 Single-Document Summarization . 824 23.4.1 Unsupervised Content Selection . 824 23.4.2 Unsupervised Summarization Based on Rhetorical Parsing . 826 23.4.3 Supervised Content Selection . 828 23.4.4 Sentence Simplification . 829 23.5 Multi-Document Summarization . 830 23.5.1 Content Selection in Multi-Document Summarization . 831 23.5.2 Information Ordering in Multi-Document Summarization . 832 23.6 Focused Summarization and Question Answering . 835 23.7 Summarization Evaluation . 839 23.8 Summary . 841 Bibliographical and Historical Notes . 842 Exercises . 844 24 Dialogue and Conversational Agents 847 24.1 Properties of Human Conversations . 849 24.1.1 Turns and Turn-Taking . 849 24.1.2 Language as Action: Speech Acts . 851 24.1.3 Language as Joint Action: Grounding . 852 24.1.4 Conversational Structure . 854 24.1.5 Conversational Implicature . 855 24.2 Basic Dialogue Systems . 857 24.2.1 ASR Component . 857 24.2.2 NLU Component . 858 24.2.3 Generation and TTS Components . 861 24.2.4 Dialogue Manager . 863 24.2.5 Dealing with Errors: Confirmation and Rejection . 867 24.3 VoiceXML . 868 24.4 Dialogue System Design and Evaluation . 872 24.4.1 Designing Dialogue Systems . 872 24.4.2 Evaluating Dialogue Systems . 872 24.5 Information-State and Dialogue Acts . 874 24.5.1 Using Dialogue Acts . 876 24.5.2 Interpreting Dialogue Acts . 877 24.5.3 Detecting Correction Acts . 880 24.5.4 Generating Dialogue Acts: Confirmation and Rejection . 881 24.6 Markov Decision Process Architecture . 882 24.7 Advanced: Plan-Based Dialogue Agents . 886 24.7.1 Plan-Inferential Interpretation and Production . 887 24.7.2 The Intentional Structure of Dialogue . 889 24.8 Summary . g9l Bibliographical and Historical Notes . 892 Contents 21 Exercises . 894 25 Machine Translation 895 25.1 Why Machine Translation Is Hard . 898 25.1.1 Typology . 898 25.1.2 Other Structural Divergences . 900 25.1.3 Lexical Divergences . 901 25.2 Classical MT and the Vauquois Triangle . 903 25.2.1 Direct Translation . 904 25.2.2 Transfer . 906 25.2.3 Combined Direct and Transfer Approaches in Classic MT . 908 25.2.4 The Interlingua Idea: Using Meaning . 909 25.3 Statistical MT . 910 25.4 P(F \E): The Phrase-Based Translation Model . 913 25.5 Alignment in MT . 915 25.5.1 IBM Model 1. 916 25.5.2 HMM Alignment . 919 25.6 Training Alignment Models . 921 25.6.1 EM for Training Alignment Models . 922 25.7 Symmetrizing Alignments for Phrase-Based MT . 924 25.8 Decoding for Phrase-Based Statistical MT . 926 25.9 MT Evaluation . 930 25.9.1 Using Human Raters . 930 25.9.2 Automatic Evaluation: BLEU . 931 25.10 Advanced: Syntactic Models for MT . 934 25.11 Advanced: IBM Model 3 and Fertility . 935 25.11.1 Training for Model 3. 939 25.12 Advanced: Log-Linear Models for MT . 939 25.13 Summary . 940 Bibliographical and Historical Notes . 941 Exercises . 943 Bibliography 945 Author Index 995 Subject Index 1007
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author	Jurafsky, Dan 1962- Martin, James H. 1959-
author_GND	(DE-588)140274952 (DE-588)140275231
author_facet	Jurafsky, Dan 1962- Martin, James H. 1959-
author_role	aut aut
author_sort	Jurafsky, Dan 1962-
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ctrlnum	(OCoLC)253202803 (DE-599)BVBBV035148790
dewey-full	410.285
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dewey-search	410.285
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dewey-tens	410 - Linguistics
discipline	Sprachwissenschaft Informatik Literaturwissenschaft
discipline_str_mv	Sprachwissenschaft Informatik Literaturwissenschaft
edition	2. ed., internat. ed.
format	Book
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id	DE-604.BV035148790
illustrated	Illustrated
index_date	2024-07-02T22:45:47Z
indexdate	2024-07-09T21:26:05Z
institution	BVB
isbn	0135041961 9780135041963
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-016956060
oclc_num	253202803
open_access_boolean
owner	DE-355 DE-BY-UBR DE-384 DE-19 DE-BY-UBM DE-20 DE-29 DE-29T DE-634 DE-91 DE-BY-TUM DE-739 DE-188 DE-706 DE-1051 DE-11 DE-2070s DE-83 DE-703 DE-91G DE-BY-TUM DE-M382
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physical	1024 S. Ill., graph. Darst.
publishDate	2009
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publisher	Pearson Education International, Prentice Hall
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series2	Prentice-Hall-series in artificial intelligence
spelling	Jurafsky, Dan 1962- Verfasser (DE-588)140274952 aut Speech and language processing an introduction to natural language processing, computational linguistics, and speech recognition Daniel Jurafsky ; James H. Martin 2. ed., internat. ed. Upper Saddle River Pearson Education International, Prentice Hall 2009 1024 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Prentice-Hall-series in artificial intelligence Hier auch später erschienene, unveränderte Nachdrucke This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing. Automatic speech recognition Computational linguistics Parsing (Computer grammar) Automatische Spracherkennung (DE-588)4003961-4 gnd rswk-swf Computerlinguistik (DE-588)4035843-4 gnd rswk-swf Digitale Sprachverarbeitung (DE-588)4233857-8 gnd rswk-swf Natürlichsprachiges System (DE-588)4284757-6 gnd rswk-swf Digitale Sprachverarbeitung (DE-588)4233857-8 s Computerlinguistik (DE-588)4035843-4 s Automatische Spracherkennung (DE-588)4003961-4 s Natürlichsprachiges System (DE-588)4284757-6 s DE-604 Martin, James H. 1959- Verfasser (DE-588)140275231 aut Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016956060&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Jurafsky, Dan 1962- Martin, James H. 1959- Speech and language processing an introduction to natural language processing, computational linguistics, and speech recognition Automatic speech recognition Computational linguistics Parsing (Computer grammar) Automatische Spracherkennung (DE-588)4003961-4 gnd Computerlinguistik (DE-588)4035843-4 gnd Digitale Sprachverarbeitung (DE-588)4233857-8 gnd Natürlichsprachiges System (DE-588)4284757-6 gnd
subject_GND	(DE-588)4003961-4 (DE-588)4035843-4 (DE-588)4233857-8 (DE-588)4284757-6
title	Speech and language processing an introduction to natural language processing, computational linguistics, and speech recognition
title_auth	Speech and language processing an introduction to natural language processing, computational linguistics, and speech recognition
title_exact_search	Speech and language processing an introduction to natural language processing, computational linguistics, and speech recognition
title_exact_search_txtP	Speech and language processing an introduction to natural language processing, computational linguistics, and speech recognition
title_full	Speech and language processing an introduction to natural language processing, computational linguistics, and speech recognition Daniel Jurafsky ; James H. Martin
title_fullStr	Speech and language processing an introduction to natural language processing, computational linguistics, and speech recognition Daniel Jurafsky ; James H. Martin
title_full_unstemmed	Speech and language processing an introduction to natural language processing, computational linguistics, and speech recognition Daniel Jurafsky ; James H. Martin
title_short	Speech and language processing
title_sort	speech and language processing an introduction to natural language processing computational linguistics and speech recognition
title_sub	an introduction to natural language processing, computational linguistics, and speech recognition
topic	Automatic speech recognition Computational linguistics Parsing (Computer grammar) Automatische Spracherkennung (DE-588)4003961-4 gnd Computerlinguistik (DE-588)4035843-4 gnd Digitale Sprachverarbeitung (DE-588)4233857-8 gnd Natürlichsprachiges System (DE-588)4284757-6 gnd
topic_facet	Automatic speech recognition Computational linguistics Parsing (Computer grammar) Automatische Spracherkennung Computerlinguistik Digitale Sprachverarbeitung Natürlichsprachiges System
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016956060&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT jurafskydan speechandlanguageprocessinganintroductiontonaturallanguageprocessingcomputationallinguisticsandspeechrecognition AT martinjamesh speechandlanguageprocessinganintroductiontonaturallanguageprocessingcomputationallinguisticsandspeechrecognition

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