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

For undergraduate or advanced undergraduate courses in Classical Natural Language Processing, Statistical Natural Language Processing, Speech Recognition, Computational Linguistics, and Human Language Processing. An explosion of Web-based language techniques, merging of distinct fields, availability of phone-based dialogue systems, and much more make this an exciting time in speech and language processing. The first of its kind to thoroughly cover language technology -- at all levels and with all modern technologies -- this text takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. The authors cover areas that traditionally are taught in different courses, to describe a unified vision of speech and language processing. Emphasis is on practical applications and scientific evaluation. An accompanying Website contains teaching materials for instructors, with pointers to language processing resources on the Web. INDICE: Foreword Preface About the Authors 1 Introduction 1.1 Knowledge inSpeech 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 ofMachine Learning: 2000--2008 1.6.7 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 Search 2.2.7 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 anFST 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-grams 4.1 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: Toolkitsand 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 Use 4.9.4 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 Tagging 5.1 (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 Combination 5.9 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 TheHidden 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 Vowels 7.2.5 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 AcousticPhonetics 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 Waveform7.4.5 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 HomographDisambiguation 8.2 Phonetic Analysis 8.2.1 Dictionary Lookup 8.2.2 Names 8.2.3 Grapheme-to-Phoneme Conversion 8.3 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 DiphoneConcatenation 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 Energy9.3.7 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 MultipassDecoding: 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 Adaptation 10.5.3 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 12.3.4Agreement 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..ETC.

• ISBN: 978-0-13-504196-3
• Editorial: Pearson
• Encuadernacion: Rústica
• Páginas: 1024
• Fecha Publicación: 28/08/2008
• Nº Volúmenes: 1
• Idioma: Inglés