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Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition, 2/e


Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition, 2/e
Author(s)  Daniel Jurafsky
ISBN  9789332518414
Imprint  Pearson Education
Copyright  2014
Pages  940
Binding  Paperback
List Price  Rs. 1325.00
  
 
 

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.

  • About the Authors
  • Contents
  • Features
  • Downloadable Resources

Dan Jurafsky is an associate professor in the Department of Linguistics, and by courtesy in Department of Computer Science, at Stanford University. Previously, he was on the faculty of the University of Colorado, Boulder, in the Linguistics and Computer Science departments and the Institute of Cognitive Science. He was born in Yonkers, New York, and received a B.A. in Linguistics in 1983 and a Ph.D. in Computer Science in 1992, both from the University of California at Berkeley. He received the National Science Foundation CAREER award in 1998 and the MacArthur Fellowship in 2002. He has published over 90 papers on a wide range of topics in speech and language processing.


 


James H. Martin is a professor in the Department of Computer Science and in the Department of Linguistics, and a fellow in the Institute of Cognitive Science at the University of Colorado at Boulder. He was born in New York City, received a B.S. in Comoputer Science from Columbia University in 1981 and a Ph.D. in Computer Science from the University of California at Berkeley in  1988. He has authored over 70 publications in computer science including the book A Computational Model of Metaphor Interpretation.


 


 

 

Contents 1. Introduction


Part I Words


2. Regular Expressions and Automata


3. Words and Transducers


4. N-grams


5. Part-of-Speech Tagging


6. Hidden Markov and Maximum Entropy Models


Part II Speech


7. Phonetics


8. Speech Synthesis


9. Automatic Speech Recognition


10. Speech Recognition: Advanced Topics


11. Computational Phonology


Part III Syntax


12. Formal Grammars of English


13. Syntactic Parsing


14. Statistical Parsing


15. Features and Unification


16. Language and Complexity


Part IV Semantics and Pragmatics


17. The Representation of Meaning


18. Computational Semantics


19. Lexical Semantics


20. Computational Lexical Semantics


21. Computational Discourse


Part V Applications


22. Information Extraction


23. Question Answering and Summarization


24. Dialogue and Conversational Agents


25. Machine Translation



 

 

• Each chapter is built around one or more worked examples demonstrating the main idea of the chapter - Uses the examples to illustrate the relative strengths and weaknesses of various approaches


• Methodology boxes included in each chapter - Introduces important methodological tools such as evaluation, wizard of oz techniques, etc.


• Problem sets included in each chapter.


• Integration of speech and text processing - Merges speech processing and natural language processing fields.


• Empiricist/statistical/machine learning approaches to language processing-Covers all of the new statistical approaches, while still completely covering the earlier more structured and rule-based methods.


• Modern rigorous evaluation metrics.


• Unified and comprehensive coverage of the field - Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language.


• Emphasis on Web and other practical applications - Gives students an understanding of how language-related algorithms can be applied to important real-world problems.


• Emphasis on scientific evaluation - Offers a description of how systems are evaluated with each problem domain.


• Description of widely available language processing resources


• Seven new chapters that extend coverage to include:


o Statistical sequence labeling


o Information extraction


o Question answering and summarization


o Advanced topics in speech recognition


o Speech synthesis


 

 
Instructor Resources
 


 
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