Speech and Language Processing

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1 Speech and Language Processing An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition Second Edition Daniel Jurafsky Stanford University James H. Martin University of Colorado at Boulder PEARSON Prentice Hall Pearson Education International

2 Contents Foreword 23 Preface 25 About the Authors 31 1 Introduction Knowledge in Speech and Language Processing Ambiguity Models and Algorithms Language, Thought, and Understanding The State of the Art Some Brief History Foundational Insights: 1940s and 1950s The Two Camps: Four Paradigms: Empiricism and Finite-State Models Redux: The Field Comes Together: The Rise of Machine Learning: On Multiple Discoveries A Final Brief Note on Psychology Summary 48 Bibliographical and Historical Notes 49 1 Words 2 Regular Expressions and Automata Regular Expressions Basic Regular Expression Patterns Disjunction, Grouping, and Precedence A Simple Example A More Complex Example Advanced Operators Regular Expression Substitution, Memory, and ELIZA Finite-State-Automata Use of an FSA to Recognize Sheeptalk Formal Languages Another Example Non-Deterministic FSAs Use of an NFSA to Accept Strings Recognition as Search Relation of Deterministic and Non-Deterministic Automata Regular Languages and FSAs Summary 75

3 10 Contents Bibliographical and Historical Notes 76 Exercises 76 Words and Transducers Survey of (Mostly) English Morphology Inflectional Morphology Derivational Morphology Cliticization Non-Concatenative Morphology Agreement Finite-State Morphological Parsing Construction of a Finite-State Lexicon Finite-State Transducers Sequential Transducers and Determinism FSTs for Morphological Parsing Transducers and Orthographic Rules The Combination of an FST Lexicon and Rules Lexicon-Free FSTs: The Porter Stemmer Word and Sentence Tokenization Segmentation in Chinese Detection and Correction of Spelling Errors Minimum Edit Distance Human Morphological Processing Ill 3.13 Summary 113 Bibliographical and Historical Notes 114 Exercises 115 TV-Grams Word Counting in Corpora Simple (Unsmoothed) TV-Grams Training and Test Sets TV-Gram Sensitivity to the Training Corpus Unknown Words: Open Versus Closed Vocabulary Tasks Evaluating TV-Grams: Perplexity Smoothing Laplace Smoothing Good-Turing Discounting Some Advanced Issues in Good-Turing Estimation Interpolation Backoff Advanced: Details of Computing Katz Backoff a and P* Practical Issues: Toolkits and Data Formats Advanced Issues in Language Modeling Advanced Smoothing Methods: Kneser-Ney Smoothing Class-Based TV-Grams Language Model Adaptation and Web Use 146

4 Contents Using Longer-Distance Information: A Brief Summary Advanced: Information Theory Background Cross-Entropy for Comparing Models Advanced: The Entropy of English and Entropy Rate Constancy Summary 153 Bibliographical and Historical Notes 154 Exercises 155 Part-of-Speech Tagging (Mostly) English Word Classes Tagsets for English Part-of-Speech Tagging Rule-Based Part-of-Speech Tagging HMM Part-of-Speech Tagging Computing the Most Likely Tag Sequence: An Example Formalizing Hidden Markov Model Taggers Using the Viterbi Algorithm for HMM Tagging Extending the HMM Algorithm to Trigrams Transformation-Based Tagging How TBL Rules Are Applied How TBL Rules Are Learned Evaluation and Error Analysis Error Analysis Advanced Issues in Part-of-Speech Tagging Practical Issues: Tag Indeterminacy and Tokenization Unknown Words Part-of-Speech Tagging for Other Languages Tagger Combination Advanced: The Noisy Channel Model for Spelling Contextual Spelling Error Correction... ' Summary 202 Bibliographical and Historical Notes..., 203 Exercises 205 Hidden Markov and Maximum Entropy Models Markov Chains The Hidden Markov Model Likelihood Computation: The Forward Algorithm Decoding: The Viterbi Algorithm HMM Training: The Forward-Backward Algorithm Maximum Entropy Models: Background Linear Regression Logistic Regression Logistic Regression: Classification Advanced: Learning in Logistic Regression Maximum Entropy Modeling 235

5 12 Contents Why We Call It Maximum Entropy Maximum Entropy Markov Models Decoding and Learning in MEMMs Summary 245 Bibliographical and Historical Notes 246 Exercises 247 II Speech 7 Phonetics Speech Sounds and Phonetic Transcription Articulatory Phonetics The Vocal Organs Consonants: Place of Articulation Consonants: Manner of Articulation Vowels Syllables Phonological Categories and Pronunciation Variation Phonetic Features Predicting Phonetic Variation Factors Influencing Phonetic Variation Acoustic Phonetics and Signals Waves Speech Sound Waves Frequency and Amplitude; Pitch and Loudness Interpretation of Phones from a Waveform Spectra and the Frequency Domain The Source-Filter Model Phonetic Resources Advanced: Articulatory and Gestural Phonology Summary 279 Bibliographical and Historical Notes 280 Exercises Speech Synthesis Text Normalization Sentence Tokenization Non-Standard Words Homograph Disambiguation Phonetic Analysis Dictionary Lookup Names Grapheme-to-Phoneme Conversion Prosodic Analysis Prosodic Structure Prosodic Prominence 297

6 Contents Tune More Sophisticated Models: ToBI Computing Duration from Prosodic Labels Computing F0 from Prosodic Labels Final Result of Text Analysis: Internal Representation Diphone Waveform Synthesis Steps for Building a Diphone Database Diphone Concatenation and TD-PSOLA for Prosody Unit Selection (Waveform) Synthesis Evaluation 314 Bibliographical and Historical Notes 315 Exercises Automatic Speech Recognition Speech Recognition Architecture The Hidden Markov Model Applied to Speech Feature Extraction: MFCC Vectors Preemphasis Windowing Discrete Fourier Transform Mel Filter Bank and Log The Cepstrum: Inverse Discrete Fourier Transform Deltas and Energy Summary: MFCC Acoustic Likelihood Computation Vector Quantization Gaussian PDFs Probabilities, Log-Probabilities, and Distance Functions The Lexicon and Language Model Search and Decoding Embedded Training Evaluation: Word Error Rate Summary 364 Bibliographical and Historical Notes 365 Exercises Speech Recognition: Advanced Topics Multipass Decoding: TV-Best Lists and Lattices A* ("Stack") Decoding Context-Dependent Acoustic Models: Triphones Discriminative Training Maximum Mutual Information Estimation Acoustic Models Based on Posterior Classifiers Modeling Variation Environmental Variation and Noise Speaker Variation and Speaker Adaptation 387

7 14 Contents Pronunciation Modeling: Variation Due to Genre Metadata: Boundaries, Punctuation, and Disfluencies Speech Recognition by Humans Summary 393 Bibliographical and Historical Notes 393 Exercises Computational Phonology Finite-State Phonology Advanced Finite-State Phonology Harmony Templatic Morphology Computational Optimality Theory Finite-State Transducer Models of Optimality Theory Stochastic Models of Optimality Theory Syllabification Learning Phonology and Morphology Learning Phonological Rules Learning Morphology Learning in Optimality Theory Summary 415 Bibliographical and Historical Notes 415 Exercises 417 III Syntax 12 Formal Grammars of English Constituency Context-Free Grammars Formal Definition of Context-Free Grammar Some Grammar Rules for English Sentence-Level Constructions Clauses and Sentences The Noun Phrase Agreement The Verb Phrase and Subcategorization Auxiliaries Coordination Treebanks Example: The Penn Treebank Project Treebanks as Grammars Treebank Searching Heads and Head Finding Grammar Equivalence and Normal Form Finite-State and Context-Free Grammars Dependency Grammars 448

8 Contents The Relationship Between Dependencies and Heads Categorial Grammar Spoken Language Syntax Disfluencies and Repair Treebanks for Spoken Language Grammars and Human Processing Summary 455 Bibliographical and Historical Notes 456 Exercises Syntactic Parsing Parsing as Search Top-Down Parsing Bottom-Up Parsing Comparing Top-Down and Bottom-Up Parsing Ambiguity Search in the Face of Ambiguity Dynamic Programming Parsing Methods CKY Parsing The Earley Algorithm Chart Parsing Partial Parsing Finite-State Rule-Based Chunking Machine Learning-Based Approaches to Chunking Chunking-System Evaluations Summary 490 Bibliographical and Historical Notes 491 Exercises Statistical Parsing Probabilistic Context-Free Grammars PCFGs for Disambiguation PCFGs for Language Modeling Probabilistic CKY Parsing of PCFGs Ways to Learn PCFG Rule Probabilities ~ Problems with PCFGs Independence Assumptions Miss Structural Dependencies Between Rules Lack of Sensitivity to Lexical Dependencies Improving PCFGs by Splitting Non-Terminals Probabilistic Lexicalized CFGs The Collins Parser Advanced: Further Details of the Collins Parser Evaluating Parsers Advanced: Discriminative Reranking Advanced: Parser-Based Language Modeling 516

9 16 Contents Human Parsing Summary 519 Bibliographical and Historical Notes 520 Exercises Features and Unification Feature Structures Unification of Feature Structures Feature Structures in the Grammar Agreement Head Features Subcategorization Long-Distance Dependencies Implementation of Unification Unification Data Structures The Unification Algorithm Parsing with Unification Constraints Integration of Unification into an Earley Parser Unification-Based Parsing Types and Inheritance Advanced: Extensions to Typing Other Extensions to Unification Summary 559 Bibliographical and Historical Notes 560 Exercises Language and Complexity The Chomsky Hierarchy Ways to Tell if a Language Isn't Regular The Pumping Lemma Proofs that Various Natural Languages Are Not Regular Is Natural Language Context Free? Complexity and Human Processing Summary 576 Bibliographical and Historical Notes 577 Exercises 578 IV Semantics and Pragmatics 17 The Representation of Meaning Computational Desiderata for Representations Verifiability Unambiguous Representations Canonical Form Inference and Variables Expressiveness 585

10 Contents Model-Theoretic Semantics First-Order Logic Basic Elements of First-Order Logic Variables and Quantifiers Lambda Notation The Semantics of First-Order Logic Inference Event and State Representations Representing Time Aspect Description Logics Embodied and Situated Approaches to Meaning Summary 614 Bibliographical and Historical Notes 614 Exercises Computational Semantics Syntax-Driven Semantic Analysis Semantic Augmentations to Syntactic Rules Quantifier Scope Ambiguity and Underspecification Store and Retrieve Approaches Constraint-Based Approaches Unification-Based Approaches to Semantic Analysis Integration of Semantics into the Earley Parser Idioms and Compositionality Summary 641 Bibliographical and Historical Notes 641 Exercises Lexical Semantics Word Senses Relations Between Senses Synonymy and Antonymy Hyponymy Semantic Fields WordNet: A Database of Lexical Relations Event Participants Thematic Roles Diathesis Alternations Problems with Thematic Roles The Proposition Bank FrameNet Selectional Restrictions Primitive Decomposition Advanced: Metaphor Summary 666

11 18 Contents Bibliographical and Historical Notes 667 Exercises Computational Lexical Semantics Word Sense Disambiguation: Overview Supervised Word Sense Disambiguation Feature Extraction for Supervised Learning Naive Bayes and Decision List Classifiers WSD Evaluation, Baselines, and Ceilings WSD: Dictionary and Thesaurus Methods The Lesk Algorithm Selectional Restrictions and Selectional Preferences Minimally Supervised WSD: Bootstrapping Word Similarity: Thesaurus Methods Word Similarity: Distributional Methods Defining a Word's Co-Occurrence Vectors Measuring Association with Context Defining Similarity Between Two Vectors Evaluating Distributional Word Similarity Hyponymy and Other Word Relations Semantic Role Labeling Advanced: Unsupervised Sense Disambiguation Summary 709 Bibliographical and Historical Notes 710 Exercises Computational Discourse Discourse Segmentation Unsupervised Discourse Segmentation Supervised Discourse Segmentation : Discourse Segmentation Evaluation Text Coherence Rhetorical Structure Theory :2.2 Automatic Coherence Assignment Reference Resolution Reference Phenomena Five Types of Referring Expressions Information Status Features for Pronominal Anaphora Resolution Features for Filtering Potential Referents Preferences in Pronoun Interpretation Three Algorithms for Anaphora Resolution Pronominal Anaphora Baseline: The Hobbs Algorithm A Centering Algorithm for Anaphora Resolution A Log-Linear Model for Pronominal Anaphora Resolution Features for Pronominal Anaphora Resolution 743

12 Contents Coreference Resolution Evaluation of Coreference Resolution Advanced: Inference-Based Coherence Resolution Psycholinguistic Studies of Reference Summary 753 Bibliographical and Historical Notes 754 Exercises 756 V Applications 22 Information Extraction Named Entity Recognition Ambiguity in Named Entity Recognition NER as Sequence Labeling Evaluation of Named Entity Recognition Practical NER Architectures Relation Detection and Classification Supervised Learning Approaches to Relation Analysis Lightly Supervised Approaches to Relation Analysis Evaluation of Relation Analysis Systems Temporal and Event Processing Temporal Expression Recognition Temporal Normalization Event Detection and Analysis TimeBank Template Filling Statistical Approaches to Template-Filling Finite-State Template-Filling Systems Advanced: Biomedical Information Extraction Biological Named Entity Recognition Gene Normalization Biological Roles and Relations Summary 796 Bibliographical and Historical Notes 796 Exercises Question Answering and Summarization Information Retrieval The Vector Space Model Term Weighting Term Selection and Creation Evaluation of Information-Retrieval Systems Homonymy, Polysemy, and Synonymy Ways to Improve User Queries Factoid Question Answering Question Processing 813

13 20 Contents Passage Retrieval Answer Processing Evaluation of Factoid Answers Summarization Single-Document Summarization Unsupervised Content Selection Unsupervised Summarization Based on Rhetorical Parsing Supervised Content Selection Sentence Simplification Multi-Document Summarization Content Selection in Multi-Document Summarization Information Ordering in Multi-Document Summarization Focused Summarization and Question Answering Summarization Evaluation Summary 841 Bibliographical and Historical Notes 842 Exercises Dialogue and Conversational Agents Properties of Human Conversations Turns and Turn-Taking Language as Action: Speech Acts Language as Joint Action: Grounding Conversational Structure Conversational Implicature Basic Dialogue Systems ASR Component NLU Component Generation and TTS Components Dialogue Manager Dealing with Errors: Confirmation and Rejection VoiceXML Dialogue System Design and Evaluation Designing Dialogue Systems Evaluating Dialogue Systems Information-State and Dialogue Acts Using Dialogue Acts Interpreting Dialogue Acts Detecting Correction Acts Generating Dialogue Acts: Confirmation and Rejection Markov Decision Process Architecture Advanced: Plan-Based Dialogue Agents Plan-Inferential Interpretation and Production The Intentional Structure of Dialogue Summary 891 Bibliographical and Historical Notes 892

14 Contents 21 Exercises Machine Translation Why Machine Translation Is Hard Typology Other Structural Divergences Lexical Divergences Classical MT and the Vauquois Triangle Direct Translation Transfer Combined Direct and Transfer Approaches in Classic MT The Interlingua Idea: Using Meaning Statistical MT P(F\E): The Phrase-Based Translation Model Alignment in MT IBM Model HMM Alignment Training Alignment Models EM for Training Alignment Models Symmetrizing Alignments for Phrase-Based MT Decoding for Phrase-Based Statistical MT MT Evaluation Using Human Raters Automatic Evaluation: BLEU Advanced: Syntactic Models for MT Advanced: IBM Model 3 and Fertility Training for Model Advanced: Log-Linear Models for MT Summary 940 Bibliographical and Historical Notes 941 Exercises Bibliography 945 Author Index 995 Subject Index 1007

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