語音與語言處理：自然語言處理、計算語言學和語音識別導論(英文版)(第2版)（簡體書）

本書全面系統地介紹了計算機自然語言處理。全書分為5個部分，共21章，深入細致地探討了計算機處理自然語言的詞匯、語法、語義、語用等各個方面的問題，介紹了自然語言處理的各種現代技術。在本書的配套網站上，還提供了相關的資源和工具，便于讀者在實踐中進一步提高。 本書不僅可以作為高等學校自然語言處理和計算語言學等課程的本科生和研究生教材，而且也是自然語言處理相關領域的研究人員和技術人員的必備參考。

(美國)Daniel Jurafsky^James H.Martin

Contents 1　Introduction　35 1.1　Knowledge in Speech and Language Processing　36 1.2　Ambiguity　38 1.3　Models andAlgorithms　39 1.4　Language, Thought, and Understanding　40 1.5　TheState of theArt　42 1.6　SomeBriefHistory　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 I　Words 2　Regular Expressions and Automata　51 2.1　RegularExpressions　51 2.1.1　Basic Regular Expression Patterns　52 2.1.2　Disjunction, Grouping, and Precedence　55 2.1.3　ASimpleExample　56 2.1.4　A More Complex Example　57 2.1.5　AdvancedOperators　58 2.1.6　Regular Expression Substitution, Memory, and ELIZA　59 2.2　Finite-StateAutomata　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 Bibliographical and Historical Notes　76 Exercises　76 3　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-StateTransducers　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　MinimumEditDistance　107 3.12　Human Morphological Processing　111 3.13　Summary　113 Bibliographical and Historical Notes　114 Exercises　115 4　N-Grams　117 4.1　WordCounting inCorpora　119 4.2　Simple (Unsmoothed) N-Grams　120 4.3　Training andTestSets　125 4.3.1　N-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　LaplaceSmoothing　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 N-Grams　145 4.9.3　Language Model Adaptation and Web Use　146 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 5　Part-of-Speech Tagging　157 5.1　(Mostly) English Word Classes　158 5.2　Tagsets forEnglish　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　ErrorAnalysis　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 6　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 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　Prosodic Analysis　296 8.3.1　Prosodic Structure　296 8.3.2　Prosodic Prominence　297 8.3.3　Tune　299 8.3.4　More Sophisticated Models: ToBI　300 8.3.5　Computing Duration from Prosodic Labels　302 8.3.6　Computing F0 from Prosodic 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 Cepstrum: 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 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 11　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-FreeGrammars　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　The Noun Phrase　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 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 andRepair　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　Problemswith PCFGs　502 14.4.1　Independence Assumptions Miss Structural Dependencies Between 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 Collins Parser　511 14.7　EvaluatingParsers　513 14.8　Advanced: Discriminative Reranking　515 14.9　Advanced: Parser-Based Language Modeling　516 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 Isnt 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 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 17.2　Model-Theoretic Semantics　586 17.3　First-OrderLogic　589 17.3.1　Basic Elements of First-Order Logic　589 17.3.2　Variables and Quantifiers　591 17.3.3　LambdaNotation　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　Selectional Restrictions　661 19.5　Primitive Decomposition　663 19.6　Advanced: Metaphor　665 19.7　Summary　666 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　Selectional Restrictions and Selectional 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 Words 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 RoleLabeling　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 21.7　Coreference Resolution　744 21.8　Evaluation of Coreference Resolution　746 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 Classi?cation　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　TermWeighting　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 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　891 Bibliographical and Historical Notes　892 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　MTEvaluation　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 forModel 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