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Speech and Language Processing (2nd Edition) [Hardcover]

Daniel Jurafsky , James H. Martin
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Table of Contents



About 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—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



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



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: 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 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



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



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



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 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

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