语音与语言处理 pdf epub mobi txt 电子书 下载 2026
出版者:人民邮电出版社
作者:Daniel Jurafsky
出品人:
页数:1024
译者:
出版时间:2010-12-5
价格:138.00元
装帧:平装
isbn号码:9787115238924
丛书系列:图灵原版计算机科学系列
图书标签:
- 自然语言处理
- NLP
- 语音识别
- 人工智能
- 计算语言学
- 机器学习
- 语音研究
- 计算机
- 语音处理
- 语言处理
- 自然语言处理
- 语音识别
- 语言识别
- 文本处理
- 人工智能
- 机器学习
- 语音合成
- 自然语言理解
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具体描述
本书是第一本从各个层面全面介绍语言技术的书，自第1版出版以来，一直好评如潮，被国外许多著名大学选为自然语言处理和计算语言学课程的主要教材。本书将深入的语言分析与健壮的统计方法结合起来，新版更是涉及了大量的现代技术，将自然语言处理、计算语言学以及语音识别等内容融合在一本书中，把各种技术相互联系起来，让读者了解怎样才能最佳地利用每种技术，怎样才能将各种技术结合起来使用。本书写作风格引人入胜，深入技术细节而又不让人感觉枯燥。
本书不仅可以作为高等学校自然语言处理和计算语言学等课程的本科生和研究生教材，对于自然语言处理相关领域的研究人员和技术人员也是不可或缺的权威参考书。
好的，这是一份针对假设的、名为《语音与语言处理》的图书内容之外的、详尽的图书简介： --- 图书名称：数字信号处理与机器学习的交叉应用 作者： [虚构作者姓名，如：陈 伟、张 丽] 出版社： [虚构出版社名称，如：创新科技出版社] ISBN： [虚构ISBN，如：978-7-12345-678-9] 内容简介 本书旨在深入探讨数字信号处理（DSP）技术与现代机器学习（ML）范式在复杂工程问题求解中的深度融合与实际应用。在当前信息爆炸的时代，对海量数据的有效解析、特征提取和模式识别能力已成为衡量技术先进性的关键指标。本书不仅仅停留在理论的阐述，更侧重于构建一个从基础理论到前沿实践的完整知识体系，尤其适合从事嵌入式系统、物联网（IoT）、高精度测量以及复杂系统建模的工程师、研究人员和高年级本科生或研究生。 全书结构严谨，分为五大部分，共十五章。 第一部分：数字信号处理基础与高级理论重构 本部分首先回顾了傅里叶分析、Z变换等DSP的基石理论，但着重于如何将这些理论从传统的时域/频域分析拓展到高维特征空间。重点章节“随机信号建模与卡尔曼滤波的现代优化”深入剖析了非线性的状态估计问题，引入了无迹卡尔曼滤波（UKF）和扩展卡尔曼滤波（EKF）在非线性系统实时跟踪中的优化策略，并通过实例展示了其在目标定位中的精度提升。此外，对小波变换（Wavelet Transform）的介绍也超越了基础的多分辨率分析，重点阐述了其在信号去噪、突变点检测中的优越性，并详细对比了离散小波变换（DWT）与连续小波变换（CWT）在不同应用场景下的适用性及计算复杂度。 第二部分：机器学习基础与深度网络架构 本部分构建了必要的机器学习理论框架，但其核心在于“面向信号数据的特征工程”与“高效网络设计”。我们着重讲解了监督学习、无监督学习及强化学习的数学基础，但更重要的是，如何根据信号的物理特性（如周期性、平稳性、稀疏性）来设计输入特征。在深度学习方面，本书详细分析了卷积神经网络（CNN）、循环神经网络（RNN）及其变体（如LSTM、GRU）的结构与梯度传播机制。特别强调的是自编码器（Autoencoders）在降维和特征学习中的应用，通过对比经典的PCA与深度稀疏自编码器，揭示了后者在高复杂度数据表示上的优势。 第三部分：DSP与ML的交叉融合：特征提取与模型训练 这是全书的核心与创新所在。本部分探讨了如何有机地结合两者的优势。我们引入了“学习型滤波器”的概念，展示了如何利用神经网络来替代或优化传统Wiener滤波器和自适应滤波器（如LMS, RLS）的设计过程，从而实现对环境变化更快的适应性。关键内容包括：如何利用CNN的卷积核学习数据的最优空间或时间特征，取代传统手工设计的梅尔频率倒谱系数（MFCC）等特征。此外，本书详细讨论了迁移学习在信号处理任务中的应用，特别是预训练模型在小样本数据集上的微调策略，这对于许多资源受限的传感器网络至关重要。我们通过一个完整的案例——高精度振动信号故障诊断，演示了从原始时域数据采集到最终分类决策的完整流水线设计。 第四部分：时间序列建模与预测的高级策略 时间序列数据的处理是信号分析的永恒主题。本部分聚焦于如何利用先进的ML技术处理具有复杂依赖性的时序数据。除了传统的ARIMA模型外，本书深入探讨了基于Attention机制的Transformer模型在长序列依赖性建模上的突破。我们提供了一种结合门控循环单元（GRU）与概率图模型的混合预测框架，旨在提高短期预测的稳定性和长期预测的合理性。在实战层面，我们详细分析了时间序列的异常检测问题，比较了基于重构误差（如Variational Autoencoders for Time Series, VAE-TS）与基于密度估计的检测方法的性能差异。 第五部分：嵌入式部署与系统优化 理论的价值最终体现在实际部署上。本书的最后一部分关注如何将复杂的DSP/ML算法高效地固化到资源受限的硬件平台上。内容涵盖了模型量化（Quantization）、剪枝（Pruning）以及知识蒸馏（Knowledge Distillation）技术，这些技术对于减小模型体积、降低推理延迟至关重要。我们提供了一系列使用TensorFlow Lite或PyTorch Mobile在特定嵌入式处理器（如DSP芯片或边缘AI加速器）上部署模型的实操指南，并详细分析了浮点运算与定点运算在精度损失与计算效率之间的权衡艺术。读者将学会如何根据目标硬件的计算资源预算，反向设计出最优的算法实现方案。 目标读者与本书特色 本书的特色在于其理论的深度、实践的广度以及跨学科的融合性。它避免了对单一领域的过度纠缠，而是将DSP的精确性与ML的泛化能力有效地耦合起来。读者通过本书，不仅能掌握前沿的信号分析技术，更能理解如何将这些技术转化为稳定、高效、可部署的工程解决方案。 ---
作者简介
Daniel Jurafsky 斯坦福大学语言学系的副教授，兼任计算机科学系教授，之前他曾任教于科罗拉多大学语言学系、计算机科学系和认知科学学院。他分别于1983年和1992年获得加利福尼亚大学伯克利分校的语言学学士学位和计算机科学博士学位。1998年获得美国国家科学基金会CAREER奖，2002年获得麦克阿瑟研究基金。他发表过90多篇语音和语言处理方面的论文。
James H. Martin 科罗拉多大学计算机科学系、语言学系教授，认知科学学院成员。他分别于1981年和1988年获得哥伦比亚大学计算机科学学士学位和加利福尼亚大学伯克利分校计算机科学博士学位。他发表过70多篇计算机科学方面的文章，著有A Computational Model of Metaphor Interpretation一书。
目录信息
Summary of Contents
Foreword 23
Preface 25
About the Authors 31
1 Introduction 35
I Words
2 Regular Expressions and Automata 51
3 Words and Transducers 79
4 N-Grams 117
5 Part-of-Speech Tagging 157
6 Hidden Markov and Maximum Entropy Models 207
7 Phonetics 249
8 Speech Synthesis 283
9 Automatic Speech Recognition 319
10 Speech Recognition: Advanced Topics 369
11 Computational Phonology 395
12 Formal Grammars of English 419
13 Syntactic Parsing 461
14 Statistical Parsing 493
15 Features and Uni?cation 523
16 Language and Complexity 563
IV Semantics and Pragmatics
17 The Representation ofMeaning 579
18 Computational Semantics 617
19 Lexical Semantics 645
20 Computational Lexical Semantics 671
21 Computational Discourse 715
V Applications
22 Information Extraction 759
23 Question Answering and Summarization 799
24 Dialogue and Conversational Agents 847
25 Machine Translation 895
Bibliography 945
Author Index 995
Subject Index 1007
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 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
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 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 In?ectional 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 MarkovChains 208
6.2 TheHiddenMarkovModel 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 LinearRegression 228
6.6.2 Logistic Regression 231
6.6.3 Logistic Regression: Classi?cation 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 TheVocalOrgans 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 In?uencing 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 TextNormalization 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 ProsodicAnalysis 296
8.3.1 ProsodicStructure 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 andEnergy 336
9.3.7 Summary:MFCC 336
9.4 Acoustic Likelihood Computation 337
9.4.1 Vector Quantization 337
9.4.2 GaussianPDFs 340
9.4.3 Probabilities, Log-Probabilities, and Distance Functions 347
9.5 The Lexicon and Language Model 348
9.6 Search andDecoding 348
9.7 EmbeddedTraining 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 DiscriminativeTraining 383
10.4.1 Maximum Mutual Information Estimation 384
10.4.2 Acoustic Models Based on Posterior Classi?ers 385
10.5 ModelingVariation 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 Dis?uencies 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 Syllabi?cation 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 De?nition 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 DependencyGrammars 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 Dis?uencies 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 asSearch 462
13.1.1 Top-DownParsing 463
13.1.2 Bottom-UpParsing 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 CKYParsing 470
13.4.2 The Earley Algorithm 477
13.4.3 ChartParsing 482
13.5 PartialParsing . 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 ProblemswithPCFGs 502
14.4.1 Independence Assumptions Miss Structural Dependencies BetweenRules 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 HumanParsing 517
14.11 Summary 519
Bibliographical and Historical Notes 520
Exercises 522
15 Features and Uni?cation 523
15.1 FeatureStructures 524
15.2 Uni?cation of Feature Structures 526
15.3 Feature Structures in the Grammar 531
15.3.1 Agreement 532
15.3.2 HeadFeatures 534
15.3.3 Subcategorization 535
15.3.4 Long-Distance Dependencies 540
15.4 Implementation of Uni?cation 541
15.4.1 Uni?cation Data Structures 541
15.4.2 The Uni?cationAlgorithm 543
15.5 Parsing with Uni?cation Constraints 547
15.5.1 Integration of Uni?cation into an Earley Parser 548
15.5.2 Uni?cation-Based Parsing 553
15.6 Types and Inheritance 555
15.6.1 Advanced: Extensions to Typing 558
15.6.2 Other Extensions to Uni?cation 559
15.7 Summary 559
Bibliographical and Historical Notes 560
Exercises 561
16 Language and Complexity 563
16.1 TheChomskyHierarchy 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
17 The Representation of Meaning 579
17.1 Computational Desiderata for Representations 581
17.1.1 Veri?ability 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 Quanti?ers . 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 RepresentingTime 600
17.4.2 Aspect 603
17.5 DescriptionLogics 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 Quanti?er Scope Ambiguity and Underspeci?cation 626
18.3.1 Store and Retrieve Approaches 626
18.3.2 Constraint-Based Approaches 629
18.4 Uni?cation-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 WordSenses 646
19.2 Relations Between Senses 649
19.2.1 Synonymy and Antonymy 649
19.2.2 Hyponymy 650
19.2.3 SemanticFields 651
19.3 WordNet: A Database of Lexical Relations 651
19.4 EventParticipants 653
19.4.1 ThematicRoles 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 Classi?ers 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 De?ning a Word’s Co-Occurrence Vectors 693
20.7.2 Measuring Association with Context 695
20.7.3 De?ning Similarity Between Two Vectors 697
20.7.4 Evaluating Distributional Word Similarity 701
20.8 Hyponymy and Other Word Relations 701
20.9 SemanticRoleLabeling 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 DiscourseSegmentation 718
21.1.1 Unsupervised Discourse Segmentation 718
21.1.2 Supervised Discourse Segmentation 720
21.1.3 Discourse Segmentation Evaluation 722
21.2 TextCoherence 723
21.2.1 Rhetorical Structure Theory 724
21.2.2 Automatic Coherence Assignment 726
21.3 ReferenceResolution 729
21.4 ReferencePhenomena 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 InformationRetrieval 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 PassageRetrieval 815
23.2.3 AnswerProcessing 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 Simpli?cation 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: Con?rmation 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: Con?rmation 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 LexicalDivergences 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 StatisticalMT 910
25.4 P(F|E): The Phrase-Based Translation Model 913
25.5 Alignment inMT 915
25.5.1 IBMModel 1 916
25.5.2 HMMAlignment 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
· · · · · · (收起)
Foreword 23
Preface 25
About the Authors 31
1 Introduction 35
I Words
2 Regular Expressions and Automata 51
3 Words and Transducers 79
4 N-Grams 117
5 Part-of-Speech Tagging 157
6 Hidden Markov and Maximum Entropy Models 207
7 Phonetics 249
8 Speech Synthesis 283
9 Automatic Speech Recognition 319
10 Speech Recognition: Advanced Topics 369
11 Computational Phonology 395
12 Formal Grammars of English 419
13 Syntactic Parsing 461
14 Statistical Parsing 493
15 Features and Uni?cation 523
16 Language and Complexity 563
IV Semantics and Pragmatics
17 The Representation ofMeaning 579
18 Computational Semantics 617
19 Lexical Semantics 645
20 Computational Lexical Semantics 671
21 Computational Discourse 715
V Applications
22 Information Extraction 759
23 Question Answering and Summarization 799
24 Dialogue and Conversational Agents 847
25 Machine Translation 895
Bibliography 945
Author Index 995
Subject Index 1007
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 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
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 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 In?ectional 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 MarkovChains 208
6.2 TheHiddenMarkovModel 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 LinearRegression 228
6.6.2 Logistic Regression 231
6.6.3 Logistic Regression: Classi?cation 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 TheVocalOrgans 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 In?uencing 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 TextNormalization 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 ProsodicAnalysis 296
8.3.1 ProsodicStructure 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 andEnergy 336
9.3.7 Summary:MFCC 336
9.4 Acoustic Likelihood Computation 337
9.4.1 Vector Quantization 337
9.4.2 GaussianPDFs 340
9.4.3 Probabilities, Log-Probabilities, and Distance Functions 347
9.5 The Lexicon and Language Model 348
9.6 Search andDecoding 348
9.7 EmbeddedTraining 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 DiscriminativeTraining 383
10.4.1 Maximum Mutual Information Estimation 384
10.4.2 Acoustic Models Based on Posterior Classi?ers 385
10.5 ModelingVariation 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 Dis?uencies 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 Syllabi?cation 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 De?nition 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 DependencyGrammars 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 Dis?uencies 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 asSearch 462
13.1.1 Top-DownParsing 463
13.1.2 Bottom-UpParsing 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 CKYParsing 470
13.4.2 The Earley Algorithm 477
13.4.3 ChartParsing 482
13.5 PartialParsing . 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 ProblemswithPCFGs 502
14.4.1 Independence Assumptions Miss Structural Dependencies BetweenRules 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 HumanParsing 517
14.11 Summary 519
Bibliographical and Historical Notes 520
Exercises 522
15 Features and Uni?cation 523
15.1 FeatureStructures 524
15.2 Uni?cation of Feature Structures 526
15.3 Feature Structures in the Grammar 531
15.3.1 Agreement 532
15.3.2 HeadFeatures 534
15.3.3 Subcategorization 535
15.3.4 Long-Distance Dependencies 540
15.4 Implementation of Uni?cation 541
15.4.1 Uni?cation Data Structures 541
15.4.2 The Uni?cationAlgorithm 543
15.5 Parsing with Uni?cation Constraints 547
15.5.1 Integration of Uni?cation into an Earley Parser 548
15.5.2 Uni?cation-Based Parsing 553
15.6 Types and Inheritance 555
15.6.1 Advanced: Extensions to Typing 558
15.6.2 Other Extensions to Uni?cation 559
15.7 Summary 559
Bibliographical and Historical Notes 560
Exercises 561
16 Language and Complexity 563
16.1 TheChomskyHierarchy 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
17 The Representation of Meaning 579
17.1 Computational Desiderata for Representations 581
17.1.1 Veri?ability 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 Quanti?ers . 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 RepresentingTime 600
17.4.2 Aspect 603
17.5 DescriptionLogics 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 Quanti?er Scope Ambiguity and Underspeci?cation 626
18.3.1 Store and Retrieve Approaches 626
18.3.2 Constraint-Based Approaches 629
18.4 Uni?cation-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 WordSenses 646
19.2 Relations Between Senses 649
19.2.1 Synonymy and Antonymy 649
19.2.2 Hyponymy 650
19.2.3 SemanticFields 651
19.3 WordNet: A Database of Lexical Relations 651
19.4 EventParticipants 653
19.4.1 ThematicRoles 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 Classi?ers 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 De?ning a Word’s Co-Occurrence Vectors 693
20.7.2 Measuring Association with Context 695
20.7.3 De?ning Similarity Between Two Vectors 697
20.7.4 Evaluating Distributional Word Similarity 701
20.8 Hyponymy and Other Word Relations 701
20.9 SemanticRoleLabeling 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 DiscourseSegmentation 718
21.1.1 Unsupervised Discourse Segmentation 718
21.1.2 Supervised Discourse Segmentation 720
21.1.3 Discourse Segmentation Evaluation 722
21.2 TextCoherence 723
21.2.1 Rhetorical Structure Theory 724
21.2.2 Automatic Coherence Assignment 726
21.3 ReferenceResolution 729
21.4 ReferencePhenomena 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 InformationRetrieval 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 PassageRetrieval 815
23.2.3 AnswerProcessing 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 Simpli?cation 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: Con?rmation 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: Con?rmation 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 LexicalDivergences 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 StatisticalMT 910
25.4 P(F|E): The Phrase-Based Translation Model 913
25.5 Alignment inMT 915
25.5.1 IBMModel 1 916
25.5.2 HMMAlignment 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
· · · · · · (收起)
读后感
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这本书的深度和宽度控制得当，适合对计算语言学和NLP各个领域都有初步的认识。来自CU Boulder的作者的组是VerbNet, Propbank和FrameNet整合者。 对于新入门的NLPer, 请务必到作者的个人主页看第三版！ [https://web.stanford.edu/~jurafsky/slp3/] 它大幅删减了对目前NLP意义没...
评分这本书的深度和宽度控制得当，适合对计算语言学和NLP各个领域都有初步的认识。来自CU Boulder的作者的组是VerbNet, Propbank和FrameNet整合者。 对于新入门的NLPer, 请务必到作者的个人主页看第三版！ [https://web.stanford.edu/~jurafsky/slp3/] 它大幅删减了对目前NLP意义没...
评分书的前面几章节很有启发性,但是后面几章理论偏多,实用性的东西稍有欠缺.总体来说还是一本难得的好书. 还有这本书设计了太多的内容,没法在这几百页里面说清楚也是必然,书后的参考文献,乖乖,好多,绝对是好东西.
评分书确实介绍了自然语言处理的方方面面，但是我个人读这本书却觉得非常的难受，不吐不快。 按理说这本书应该是可以面向初学者，当做教材使用的。而且这本书确实也是我们自然语言处理课程老师推荐的阅读教材。然而读起来我却觉得特别的难。 倒不是这本书的内容有多么的艰深，事实...
评分很不错的一本书，作者很权威，内容很全面，深度适当。 也许对某些问题不是非常的深入，但是几乎囊括了自然语言处理的方方面面。 做搜索引擎、信息检索方面的同志也可以了解下。
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这本书的封面设计非常吸引人，简约中透露着专业感，那种深邃的蓝色调仿佛在邀请我进入一个充满未知与探索的知识殿堂。我原以为这是一本偏向于纯理论的学术著作，拿到手后才发现它的内容组织极其贴合实际应用。开篇对基础概念的梳理非常扎实，作者没有急于抛出复杂的模型，而是循序渐进地构建起对“声”与“意”之间关系的宏观理解。特别是其中关于语音信号预处理的那一章，图文并茂地展示了傅里叶变换和梅尔频率倒谱系数（MFCCs）的推导过程，那份清晰度简直是教科书级别的范本。我个人非常欣赏作者在讲解算法时所采用的类比方法，比如将声学特征比作人类的“指纹”，将语言模型比作“语境的记忆库”，这些生动的比喻极大地降低了入门的心理门槛。读完前三分之一，我已经能够自信地与同事讨论当前主流的语音识别框架的优缺点，这在以前是不可想象的。这本书的价值在于，它不仅告诉你“是什么”，更重要的是，它细致地剖析了“为什么是这样”以及“如何才能做得更好”。对于任何想在人工智能领域深耕，尤其是对人机交互界面感兴趣的研究者或工程师来说，这都是一本必备的“内功心法”。
评分坦白说，我最初拿到这本书时，是抱着怀疑态度的，因为市面上关于这个主题的教材汗牛充栋，大多内容陈旧或过于偏颇。然而，这本书的出现，彻底改变了我的看法。它的论述视角非常独特，几乎是从“信息论”和“认知科学”的交叉点来审视语音和语言的本质。比如，书中对语用学和句法学的结合分析，远比我大学时学的任何一本语言学教材都要精妙和深刻。它没有将语音和语言割裂开来处理，而是强调二者在信息编码和解码过程中的相互依赖关系。这种系统性的思维方式，让我对“理解”的含义有了全新的认识。读到最后，我感觉自己不仅仅是学会了如何训练一个模型，更是对人类自身的交流机制产生了更深层次的敬畏。其中关于情感计算和语音韵律分析的部分，简直是为心理学和人机交互领域的研究者量身定做的宝藏章节，它揭示了“如何让机器听懂‘言外之意’”的奥秘。
评分这本书的排版和印刷质量堪称一流，这对于一本需要大量数学公式和图表的理工科书籍来说至关重要。字体选择恰当，行距和页边距的留白设计合理，长时间阅读下来眼睛不易疲劳。我记得有一处关于隐马尔可夫模型（HMM）的推导，涉及复杂的概率公式和转移矩阵，如果排版混乱，光是看懂符号的上下标就会让人抓狂，但在这本书里，每一个公式都被清晰地居中、编号，逻辑链条清晰可见。这体现了出版方对知识的尊重，以及对读者阅读体验的极致追求。而且，随书附带的在线资源库也非常实用，提供了大量的公开数据集链接和参考代码库，这极大地拓宽了读者的实践空间。我特别赞赏作者在每一章节末尾设置的“进一步阅读”推荐列表，这些推荐的书目和论文都极具前瞻性，为我后续的研究指明了方向。这本书的物理形态本身，就是一种高品质知识载体的体现。
评分这本书的实战指导性是我在众多技术书籍中见过的最强之一。它不仅仅停留在理论的描述上，而是像一位经验丰富的老教授带着你做项目。我尤其对其中关于深度学习在自然语言处理（NLP）中应用的章节印象深刻。作者巧妙地将循环神经网络（RNNs）、长短期记忆网络（LSTMs）乃至后来的Transformer架构的演变脉络梳理得井井有条。书中提供的代码示例虽然是概念性的，但其结构设计和模块划分逻辑清晰到令人拍案叫绝。我尝试着按照书中的步骤搭建了一个简单的文本分类模型，结果在处理特定领域语料时，性能提升立竿见影。更难能可贵的是，作者在讨论每个模型时，都会穿插讲解其局限性和当前研究的热点方向，这使得我们不至于陷入对过时技术的迷恋。整本书的叙事节奏把握得非常好，既有深度，又不失广度，让你感觉每翻一页，都能从书本中汲取到解决实际问题的“弹药”。对于那些渴望将理论知识快速转化为生产力的开发者而言，这本书无疑是加速成长的“催化剂”。
评分这本书最让我感到惊喜的是它对“未来趋势”的洞察力，丝毫没有那种“写完就过时”的滞后感。在讨论完当前主流的序列到序列（Seq2Seq）模型后，作者花了相当大的篇幅去探讨多模态融合的必要性。书中对文本到语音（TTS）合成中，如何融入情感、音色个性化，以及如何应对“低资源语言”挑战的分析，展现了作者站在行业前沿的视野。例如，关于零样本学习（Zero-shot Learning）在语音识别中的应用展望，虽然目前尚处于实验室阶段，但作者的论述逻辑严密，预测性极强，让人对AI的下一步发展充满期待。它不仅仅是一本技术手册，更像是一份面向未来的“技术路线图”。阅读它，我感觉自己仿佛提前解锁了未来几年该领域可能出现的新范式。对于希望站在技术制高点、引领行业发展的人来说，这本书的战略指导价值，甚至超越了其具体的算法细节描述。
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