Statistical Language Learning

Eugene Charniak breaks new ground in artificial intelligenceresearch 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 wordclasses, 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

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.

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Charniak (computer science, Brown U.) presents statistical language processing from an artificial intelligence point of view in a text for researchers and scientists with a traditional computer science background. Annotation c. Book News, Inc., Portland, OR (booknews.com)