Statistical Language Learning

Front Cover
MIT Press, 1996 - Computers - 170 pages
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Eugene Charniak breaks new ground in artificial intelligence research by presenting statistical language processing from an artificial intelligence point of view in a text for researchers and scientists with a traditional computer science background.

New, exacting empirical methods are needed to break the deadlock in such areas of artificial intelligence as robotics, knowledge representation, machine learning, machine translation, and natural language processing (NLP). It is time, Charniak observes, to switch paradigms. This text introduces statistical language processing techniques -- word tagging, parsing with probabilistic context free grammars, grammar induction, syntactic disambiguation, semantic word classes, word-sense disambiguation -- along with the underlying mathematics and chapter exercises.

Charniak points out that as a method of attacking NLP problems, the statistical approach has several advantages. It is grounded in real text and therefore promises to produce usable results, and it offers an obvious way to approach learning: "one simply gathers statistics."

"Language, Speech, and Communication"

  

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Review: Statistical Language Learning

User Review  - Leif - Goodreads

I had this book sitting on my shelf for several months without realizing that it's a wonderfully brief, thorough, and understandable introduction to several important natural language processing techniques. An excellent introduction for computer science folks looking to get into NLP. Read full review

Contents

The Standard Model
1
Statistical Models apd the Entropy of English
21
Hidden Markov Models and Two Applications
39
Algorithms for Hidden Markov Models
53
Probabilistic ContextFree Grammars
75
The Mathematics of PCFGs
87
Learning Probabilistic Grammars
103
Syntactic Disambiguation
119
Word Senses and Their Disambiguation
147
Bibliography
163
Glossary
165
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