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ForewordPrefaceAbout the Authors  1 Introduction 1.1 Knowledge in Speech and Language Processing 1.2 Ambiguity 1.3 Models and Algorithms 1.4 Language, Thought, and Understanding 1.5 The State of the Art 1.6 Some Brief History 1.6.1 Foundational Insights: 1940s and 1950s 1.6.2 The Two Camps: 1957—1970 1.6.3 Four Paradigms: 1970—1983 1.6.4 Empiricism and Finite State Models Redux: 1983—1993 1.6.5 The Field Comes Together: 1994—1999 1.6.6 The Rise of Machine Learning: 2000—20081.6.7 On Multiple Discoveries 1.6.8 A Final Brief Note on Psychology 1.7 Summary Bibliographical and Historical Notes  Part I Words 2 Regular Expressions and Automata 2.1 Regular Expressions 2.1.1 Basic Regular Expression Patterns 2.1.2 Disjunction, Grouping, and Precedence 2.1.3 A Simple Example 2.1.4 A More Complex Example 2.1.5 Advanced Operators 2.1.6 Regular Expression Substitution, Memory, and ELIZA 2.2 Finite-State Automata 2.2.1 Using an FSA to Recognize Sheeptalk 2.2.2 Formal Languages 2.2.3 Another Example 2.2.4 Non-Deterministic FSAs 2.2.5 Using an NFSA to Accept Strings 2.2.6 Recognition as Search2.2.7 Relating Deterministic and Non-Deterministic Automata 2.3 Regular Languages and FSAs 2.4 Summary Bibliographical and Historical Notes Exercises  3 Words and Transducers 3.1 Survey of (Mostly) English Morphology 3.1.1 Inflectional Morphology 3.1.2 Derivational Morphology 3.1.3 Cliticization 3.1.4 Non-Concatenative Morphology 3.1.5 Agreement 3.2 Finite-State Morphological Parsing 3.3 Construction of a Finite-State Lexicon 3.4 Finite-State Transducers 3.4.1 Sequential Transducers and Determinism 3.5 FSTs for Morphological Parsing 3.6 Transducers and Orthographic Rules 3.7 The COmbination of an FST Lexicon and Rules 3.8 Lexicon-Free FSTs: The Porter Stemmer 3.9 Word and Sentence Tokenization 3.9.1 Segmentation in Chinese 3.10 Detection and Correction of Spelling Errors 3.11 Minimum Edit Distance 3.12 Human Morphological Processing 3.13 Summary Bibliographical and Historical Notes Exercises  4 N-grams4.1 Word Counting in Corpora 4.2 Simple (Unsmoothed) N-grams 4.3 Training and Test Sets 4.3.1 N-gram Sensitivity to the Training Corpus 4.3.2 Unknown Words: Open Versus Closed Vocabulary Tasks 4.4 Evaluating N-grams: Perplexity 4.5 Smoothing 4.5.1 Laplace Smoothing 4.5.2 Good-Turing Discounting 4.5.3 Some Advanced Issues in Good-Turing Estimation 4.6 Interpolation 4.7 Backoff 4.7.1 Advanced: Details of Computing Katz Backoff a and P* 4.8 Practical Issues: Toolkits and Data Formats 4.9 Advanced Issues in Language Modeling 4.9.1 Advanced Smoothing Methods: Kneser-Ney Smoothing 4.9.2 Class-Based N-grams 4.9.3 Language Model Adaptation and Web Use4.9.4 Using Longer Distance Information: A Brief Summary 4.10 Advanced: Information Theory Background 4.10.1 Cross-Entropy for Comparing Models 4.11 Advanced: The Entropy of English and Entropy Rate Constancy 4.12 Summary Bibliographical and Historical Notes Exercises  5 Part-of-Speech Tagging5.1 (Mostly) English Word Classes 5.2 Tagsets for English 5.3 Part-of-Speech Tagging 5.4 Rule-Based Part-of-Speech Tagging 5.5 HMM Part-of-Speech Tagging 5.5.1 Computing the Most-Likely Tag Sequence: An Example 5.5.2 Formalizing Hidden Markov Model Taggers 5.5.3 Using the Viterbi Algorithm for HMM Tagging 5.5.4 Extending the HMM Algorithm to Trigrams 5.6 Transformation-Based Tagging 5.6.1 How TBL Rules Are Applied 5.6.2 How TBL Rules Are Learned 5.7 Evaluation and Error Analysis 5.7.1 Error Analysis 5.8 Advanced Issues in Part-of-Speech Tagging 5.8.1 Practical Issues: Tag Indeterminacy and Tokenization 5.8.2 Unknown Words 5.8.3 Part-of-Speech Tagging for Other Languages 5.8.4 Tagger Combination5.9 Advanced: The Noisy Channel Model for Spelling 5.9.1 Contextual Spelling Error Correction 5.10 Summary Bibliographical and Historical Notes Exercises  6 Hidden Markov and Maximum Entropy Models 6.1 Markov Chains 6.2 The Hidden Markov Model 6.3 Likelihood Computation: The Forward Algorithm 6.4 Decoding: The Viterbi Algorithm 6.5 HMM Training: The Forward-Backward Algorithm 6.6 Maximum Entropy Models: Background 6.6.1 Linear Regression 6.6.2 Logistic Regression 6.6.3 Logistic Regression: Classification 6.6.4 Advanced: Learning in Logistic Regression 6.7 Maximum Entropy Modeling 6.7.1 Why We Call it Maximum Entropy 6.8 Maximum Entropy Markov Models 6.8.1 Decoding and Learning in MEMMs 6.9 Summary Bibliographical and Historical Notes Exercises Part II Speech 7 Phonetics 7.1 Speech Sounds and Phonetic Transcription 7.2 Articulatory Phonetics 7.2.1 The Vocal Organs 7.2.2 Consonants: Place of Articulation 7.2.3 Consonants: Manner of Articulation 7.2.4 Vowels7.2.5 Syllables 7.3 Phonological Categories and Pronunciation Variation 7.3.1 Phonetic Features 7.3.2 Predicting Phonetic Variation 7.3.3 Factors Influencing Phonetic Variation 7.4 Acoustic Phonetics and Signals 7.4.1 Waves 7.4.2 Speech Sound Waves 7.4.3 Frequency and Amplitude; Pitch and Loudness 7.4.4 Interpretation of Phones from a Waveform 7.4.5 Spectra and the Frequency Domain 7.4.6 The Source-Filter Model 7.5 Phonetic Resources 7.6 Advanced: Articulatory and Gestural Phonology 7.7 Summary Bibliographical and Historical Notes Exercises  8 Speech Synthesis 8.1 Text Normalization 8.1.1 Sentence Tokenization 8.1.2 Non-Standard Words 8.1.3 Homograph Disambiguation 8.2 Phonetic Analysis 8.2.1 Dictionary Lookup 8.2.2 Names 8.2.3 Grapheme-to-Phoneme Conversion8.3 Prosodic Analysis 8.3.1 Prosodic Structure 8.3.2 Prosodic Prominence 8.3.3 Tune 8.3.4 More Sophisticated Models: ToBI 8.3.5 Computing Duration from Prosodic Labels 8.3.6 Computing F0 from Prosodic Labels 8.3.7 Final Result of Text Analysis: Internal Representation 8.4 Diphone Waveform synthesis 8.4.1 Steps for Building a Diphone Database 8.4.2 Diphone Concatenation and TD-PSOLA for Prosody 8.5 Unit Selection (Waveform) Synthesis 8.6 Evaluation Bibliographical and Historical Notes Exercises  9 Automatic Speech Recognition 9.1 Speech Recognition Architecture 9.2 Applying the Hidden Markov Model to Speech 9.3 Feature Extraction: MFCC vectors 9.3.1 Preemphasis 9.3.2 Windowing 9.3.3 Discrete Fourier Transform 9.3.4 Mel Filter Bank and Log 9.3.5 The Cepstrum: Inverse Discrete Fourier Transform 9.3.6 Deltas and Energy 9.3.7 Summary: MFCC 9.4 Acoustic Likelihood Computation 9.4.1 Vector Quantization 9.4.2 Gaussian PDFs 9.4.3 Probabilities, Log Probabilities and Distance Functions 9.5 The Lexicon and Language Model 9.6 Search and Decoding 9.7 Embedded Training 9.8 Evaluation: Word Error Rate 9.9 Summary Bibliographical and Historical Notes Exercises  10 Speech Recognition: Advanced Topics 10.1 Multipass Decoding: N-best Lists and Lattices 10.2 A* (‘Stack’) Decoding 10.3 Context-Dependent Acoustic Models: Triphones 10.4 Discriminative Training 10.4.1 Maximum Mutual Information Estimation 10.4.2 Acoustic Models Based on Posterior Classifiers 10.5 Modeling Variation 10.5.1 Environmental Variation and Noise 10.5.2 Speaker Variation and Speaker Adaptation10.5.3 Pronunciation Modeling: Variation Due to Genre 10.6 Metadata: Boundaries, Punctuation, and Disfluencies 10.7 Speech Recognition by Humans 10.8 Summary Bibliographical and Historical Notes Exercises  11 Computational Phonology 11.1 Finite-State Phonology 11.2 Advanced Finite-State Phonology 11.2.1 Harmony 11.2.2 Templatic Morphology 11.3 Computational Optimality Theory 11.3.1 Finite-State Transducer Models of Optimality Theory 11.3.2 Stochastic Models of Optimality Theory 11.4 Syllabification 11.5 Learning Phonology and Morphology 11.5.1 Learning Phonological Rules 11.5.2 Learning Morphology 11.5.3 Learning in Optimality Theory 11.6 Summary Bibliographical and Historical Notes Exercises  Part III Syntax 12 Formal Grammars of English 12.1 Constituency 12.2 Context-Free Grammars 12.2.1 Formal definition of Context-Free Grammar 12.3 Some Grammar Rules for English 12.3.1 Sentence-Level Constructions 12.3.2 Clauses and Sentences 12.3.3 The Noun Phrase 12.3.4 Agreement 12.3.5 The Verb Phrase and Subcategorization 12.3.6 Auxiliaries 12.3.7 Coordination 12.4 Treebanks 12.4.1 Example: The Penn Treebank Project 12.4.2 Treebanks as Grammars 12.4.3 Treebank Searching12.4.4 Heads and Head Finding 12.5 Grammar Equivalence and Normal Form 12.6 Finite-State and Context-Free Grammars 12.7 Dependency Grammars 12.7.1 The Relationship Between Dependencies and Heads 12.7.2 Categorial Grammar 12.8 Spoken Language Syntax 12.8.1 Disfluencies and Repair 12.8.2 Treebanks for Spoken Language 12.9 Grammars and Human Processing 12.10 Summary Bibliographical and Historical Notes Exercises  13 Syntactic Parsing13.1 Parsing as Search 13.1.1 Top-Down Parsing 13.1.2 Bottom-Up Parsing 13.1.3 Comparing Top-Down and Bottom-Up Parsing 13.2 Ambiguity 13.3 Search in the Face of Ambiguity 13.4 Dynamic Programming Parsing Methods 13.4.1 CKY Parsing 13.4.2 The Earley Algorithm 13.4.3 Chart Parsing 13.5 Partial Parsing 13.5.1 Finite-State Rule-Based Chunking 13.5.2 Machine Learning-Based Approaches to Chunking 13.5.3 Evaluating Chunking Systems 13.6 Summary Bibliographical and Historical Notes Exercises  14 Statistical Parsing 14.1 Probabilistic Context-Free Grammars 14.1.1 PCFGs for Disambiguation 14.1.2 PCFGs for Language Modeling 14.2 Probabilistic CKY Parsing of PCFGs 14.3 Learning PCFG Rule Probabilities 14.4 Problems with PCFGs 14.4.1 Independence Assumptions Miss Structural Dependencies Between Rules 14.4.2 Lack of Sensitivity to Lexical Dependencies 14.5 Improving PCFGs by Splitting Non-Terminals 14.6 Probabilistic Lexicalized CFGs 14.6.1 The Collins Parser 14.6.2 Advanced: Further Details of the Collins Parser 14.7 Evaluating Parsers 14.8 Advanced: Discriminative Reranking 14.9 Advanced: Parser-Based Language Modeling 14.10 Human Parsing 14.11 Summary Bibliographical and Historical Notes Exercises  15 Features and Unification 15.1 Feature Structures 15.2 Unification of Feature Structures 15.3 Feature Structures in the Grammar 15.3.1 Agreement 15.3.2 Head Features 15.3.3 Subcategorization 15.3.4 Long-Distance Dependencies 15.4 Implementation of Unification 15.4.1 Unification Data Structures 15.4.2 The Unification Algorithm 15.5 Parsing with Unification Constraints 15.5.1 Integration of Unification into an Earley Parser 15.5.2 Unification-Based Parsing 15.6 Types and Inheritance 15.6.1 Advanced: Extensions to Typing 15.6.2 Other Extensions to Unification 15.7 Summary Bibliographical and Historical Notes Exercises  16 Language and Complexity 16.1 The Chomsky Hierarchy 16.2 Ways to Tell if a Language Isn’t Regular 16.2.1 The Pumping Lemma 16.2.2 Proofs That Various Natural Languages Are Not Regular 16.3 Is Natural Language Context-Free? 16.4 Complexity and Human Processing 16.5 Summary Bibliographical and Historical Notes Exercises  Part IV Semantics and Pragmatics 17 The Representation of Meaning 17.1 Computational Desiderata for Representations 17.1.1 Verifiability 17.1.2 Unambiguous Representations 17.1.3 Canonical Form 17.1.4 Inference and Variables 17.1.5 Expressiveness 17.2 Model-Theoretic Semantics 17.3 First-Order Logic 17.3.1 Basic Elements of First-Order Logic 17.3.2 Variables and Quantifiers 17.3.3 Lambda Notation 17.3.4 The Semantics of First-Order Logic 17.3.5 Inference 17.4 Event and State Representations 17.4.1 Representing Time 17.4.2 Aspect 17.5  Description Logics 17.6 Embodied and Situated Approaches to Meaning 17.7 Summary Bibliographical and Historical Notes Exercises  18 Computational Semantics 18.1 Syntax-Driven Semantic Analysis 18.2 Semantic Augmentations to Syntactic Rules 18.3 Quantifier Scope Ambiguity and Underspecification 18.3.1 Store and Retrieve Approaches 18.3.2 Constraint-Based Approaches18.4 Unification-Based Approaches to Semantic Analysis 18.5 Integration of Semantics into the Earley Parser 18.6 Idioms and Compositionality 18.7 Summary Bibliographical and Historical Notes Exercises  19 Lexical Semantics 19.1 Word Senses 19.2 Relations Between Senses 19.2.1 Synonymy and Antonymy 19.2.2 Hyponymy 19.2.3 Semantic Fields 19.3 WordNet: A Database of Lexical Relations 19.4 Event Participants19.4.1 Thematic Roles 19.4.2 Diathesis Alternations 19.4.3 Problems with Thematic Roles 19.4.4 The Proposition Bank 19.4.5 FrameNet 19.4.6 Selectional Restrictions 19.5 Primitive Decomposition 19.6 Advanced: Metaphor 19.7 Summary Bibliographical and Historical Notes Exercises  20 Computational Lexical Semantics 20.1 Word Sense Disambiguation: Overview 20.2 Supervised Word Sense Disambiguation 20.2.1 Feature Extraction for Supervised Learning 20.2.2 Naive Bayes and Decision List Classifiers 20.3 WSD Evaluation, Baselines, and Ceilings 20.4 WSD: Dictionary and Thesaurus Methods 20.4.1 The Lesk Algorithm 20.4.2 Selectional Restrictions and Selectional Preferences . . . . 68420.5 Minimally Supervised WSD: Bootstrapping 20.6 Word Similarity: Thesaurus Methods 20.7 Word Similarity: Distributional Methods 20.7.1 Defining a Word’s Co-Occurrence Vectors 20.7.2 Measuring Association with Context 20.7.3 Defining Similarity Between Two Vectors 20.7.4 Evaluating Distributional Word Similarity 20.8 Hyponymy and Other Word Relations 20.9 Semantic Role Labeling 20.10 Advanced: Unsupervised Sense Disambiguation 20.11 SummaryBibliographical and Historical Notes Exercises  21 Computational Discourse 21.1 Discourse Segmentation 21.1.1 Unsupervised Discourse Segmentation 21.1.2 Supervised Discourse Segmentation 21.1.3 Discourse Segmentation Evaluation21.2 Text Coherence 21.2.1 Rhetorical Structure Theory 21.2.2 Automatic Coherence Assignment 21.3 Reference Resolution 21.4 Reference Phenomena 21.4.1 Five Types of Referring Expressions 21.4.2 Information Status 21.5 Features for Pronominal Anaphora Resolution 21.6 Three Algorithms for Pronominal Anaphora Resolution 21.6.1 Pronominal Anaphora Baseline: The Hobbs Algorithm 21.6.2 A Centering Algorithm for Anaphora Resolution 21.6.3 A Log-Linear Model for Pronominal Anaphora Resoluton 21.6.4 Features for Pronominal Anaphora Resoluton21.7 Coreference Resolution 21.8 Evaluation of Coreference Resolution 21.9 Advanced: Inference-Based Coherence Resolution 21.10 Psycholinguistic Studies of Reference21.11 Summary Bibliographical and Historical Notes Exercises  Part V Applications 22 Information Extraction 22.1 Named Entity Recognition 22.1.1 Ambiguity in Named Entity Recognition 22.1.2 NER as Sequence Labeling 22.1.3 Evaluation of Named Entity Recognition 22.1.4 Practical NER Architectures 22.2 Relation Detection and Classification 22.2.1 Supervised Learning Approaches to Relation Analysis 22.2.2 Lightly Supervised Approaches to Relation Analysis 22.2.3 Evaluation of Relation Analysis Systems 22.3 Temporal and Event Processing 22.3.1 Temporal Expression Recognition 22.3.2 Temporal Normalization 22.3.3 Event Detection and Analysis 22.3.4 TimeBank 22.4 Template-Filling 22.4.1 Statistical Approaches to Template-Filling 22.4.2 Finite-State Template-Filling Systems 22.5 Advanced: Biomedical Information Extraction 22.5.1 Biological Named Entity Recognition 22.5.2 Gene Normalization 22.5.3 Biological Roles and Relations 22.6 Summary Bibliographical and Historical Notes Exercises  23 Question Answering and Summarization 23.1 Information Retrieval 23.1.1 The Vector Space Model 23.1.2 Term Weighting 23.1.3 Term Selection and Creation 23.1.4 Evaluation of Information-Retrieval Systems 23.1.5 Homonymy, Polysemy, and Synonymy 23.1.6 Ways to Improve User Queries 23.2 Factoid Question Answering 23.2.1 Question Processing 23.2.2 Passage Retrieval 23.2.3 Answer Processing 23.2.4 Evaluation of Factoid Answers 23.3 Summarization 23.4 Single Document Summarization 23.4.1 Unsupervised Content Selection23.4.2 Unsupervised Summarization Based on Rhetorical Parsing23.4.3 Supervised Content Selection23.4.4 Sentence Simplification23.5 Multi-Document Summarization 23.5.1 Content Selection in Multi-Document Summarization 23.5.2 Information Ordering in Multi-Document Summarization23.6 Focused Summarization and Question Answering23.7 Summarization Evaluation 23.8 Summary Bibliographical and Historical Notes Exercises  24 Dialogue and Conversational Agents 24.1 Properties of Human Conversations 24.1.1 Turns and Turn-Taking 24.1.2 Language as Action: Speech Acts 24.1.3 Language as Joint Action: Grounding 24.1.4 Conversational Structure 24.1.5 Conversational Implicature 24.2 Basic Dialogue Systems 24.2.1 ASR component 24.2.2 NLU component 24.2.3 Generation and TTS components 24.2.4 Dialogue Manager 24.2.5 Dealing with Errors: Confirmation and Rejection 24.3 VoiceXML 24.4 Dialogue System Design and Evaluation 24.4.1 Designing Dialogue Systems 24.4.2 Evaluating Dialogue Systems24.5 Information-State and Dialogue Acts 24.5.1 Using Dialogue Acts 24.5.2 Interpreting Dialogue Acts 24.5.3 Detecting Correction Acts 24.5.4 Generating Dialogue Acts: Confirmation and Rejection24.6 Markov Decision Process Architecture 24.7 Advanced: Plan-Based Dialogue Agents 24.7.1 Plan-Inferential Interpretation and Production 24.7.2 The Intentional Structure of Dialogue 24.8 Summary Bibliographical and Historical Notes Exercises  25 Machine Translation 25.1 Why Machine Translation Is Hard25.1.1 Typology 25.1.2 Other Structural Divergences 25.1.3 Lexical Divergences 25.2 Classical MT and the Vauquois Triangle 25.2.1 Direct Translation 25.2.2 Transfer 25.2.3 Combined Direct and Tranfer Approaches in Classic MT 25.2.4 The Interlingua Idea: Using Meaning 25.3 Statistical MT 25.4 P(F|E): the Phrase-Based Translation Model 25.5 Alignment in MT 25.5.1 IBM Model 1 25.5.2 HMM Alignment 25.6 Training Alignment Models 25.6.1 EM for Training Alignment Models 25.7 Symmetrizing Alignments for Phrase-Based MT 25.8 Decoding for Phrase-Based Statistical MT 25.9 MT Evaluation 25.9.1 Using Human Raters 25.9.2 Automatic Evaluation: BLEU25.10 Advanced: Syntactic Models for MT 25.11 Advanced: IBM Model 3 and Fertitlity25.11.1 Training for Model 3 25.12 Advanced: Log-linearModels for MT . . . . . . . . . . . . . . . . 90325.13 SummaryBibliographical and Historical Notes Exercises  Bibliography Author Index Subject Index   

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

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