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SPEECH and LANGUAGE PROCESSING An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition Second Edition by Daniel Jurafsky and James H. Martin Last Update January 6, 2009: The 2nd edition is now avaiable. A million thanks to everyone who sent us corrections and suggestions for all the draft chapters.
The Handbook of Natural Language Processing, Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as sentiment analysis.
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Description
This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book.
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. The Second Edition offers a significant amount of new and extended material.
Supplements:
Click on the 'Resources' tab to View Downloadable Files:
Power Point Lecture Slides - Chapters 1-5, 8-10, 12-13 and 24 Now Available!
For additional resourcse visit the author website: http://www.cs.colorado.edu/~martin/slp.html
Table of Contents
Foreword
Preface
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
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-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∗
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 Selection
23.4.2 Unsupervised Summarization Based on Rhetorical Parsing
23.4.3 Supervised Content Selection
23.4.4 Sentence Simplification
23.5 Multi-Document Summarization
23.5.1 Content Selection in Multi-Document Summarization
23.5.2 Information Ordering in Multi-Document Summarization
23.6 Focused Summarization and Question Answering
23.7 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
Speech And Language Processing 2nd Edition By Daniel Jurafsky And James H Martin
24.4.1 Designing Dialogue Systems
24.4.2 Evaluating Dialogue Systems
24.5 Information-State and Dialogue Acts
24.5.1 Using Dialogue Acts
24.5.2 Interpreting Dialogue Acts
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24.5.3 Detecting Correction Acts
24.5.4 Generating Dialogue Acts: Confirmation and Rejection
24.6 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 Hard
25.1.1 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