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

Front Cover
Prentice Hall, 2009 - Computers - 988 pages

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:

  • Solutions
  • 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

From inside the book

 

Results 1-3 of 94

Page 179
... Likelihood Computation : The Forward Algorithm Our first problem is to compute the likelihood of a particular observation sequence . For example , given the HMM in Fig . 6.2b , what is the probability of the sequence 3 1 3 ? More ...
Page 303
... likelihood of these feature vectors given an HMM state . Recall from Chapter 6 that this output likelihood is computed by the B probability function of the HMM . Given an individual state q ; and an observation of , the observation ...
Page 311
... likelihood bj ( ot ) of a D - dimensional fea- ture vector of given HMM state j , using a diagonal - covariance multivariate Gaussian . D bj ( 01 ) = II 2πσ ? d = 1 1 exp jd ( 1 1 , ( Ord - μja ) 2 σjd2 ( 9.34 ) Training a diagonal ...

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Other editions - View all

Speech and Language Processing: An Introduction to Natural Language ...
Daniel Jurafsky,James H. Martin
No preview available - 2009

Common terms and phrases

acoustic algorithm alignment ambiguity applied approach bigram called Chapter classifier coherence complex Computational Linguistics constituent constraints context context-free grammars corpus decoding dictionary diphone disambiguation discourse documents English entropy evaluation example extraction feature structures Figure finite-state flight formal frequency given hidden Markov model input introduced labeled language model language processing lexical lexicon likelihood machine learning machine translation Markov meaning representation methods morphological N-gram named entity natural language node Nominal non-terminal noun phrase parse tree parser part-of-speech tagging phonological predict probabilistic probability problem pronoun pronunciation question reference regular expression relations represent Restaurant role rules segmentation semantic sentence sequence shows speaker specific speech and language speech recognition speech synthesis statistical string summarization symbols syntactic tagger tagset task test set tion training set transducer Treebank unification values vector verb Viterbi Viterbi algorithm vowel word sense WordNet

References to this book

Bibliographic information

TitleSpeech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Prentice Hall series in artificial intelligence
AuthorsDan Jurafsky, James H. Martin
Editionillustrated
PublisherPrentice Hall, 2009
ISBN0131873210, 9780131873216
Length988 pages
Subjects ›  › 

Computers / Artificial Intelligence / Computer Vision & Pattern Recognition
Computers / Artificial Intelligence / General
Computers / Artificial Intelligence / Natural Language Processing
Computers / Optical Data Processing
Computers / Speech & Audio Processing
Language Arts & Disciplines / Linguistics / General
Technology & Engineering / Electrical
  
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