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 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."
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Eugene Charniak is Professor and Chairman, Department of Computer Science, Brown University.
This text presents statistical language processing from an artificial intelligence point of view intended for researchers and scientists with a traditional computer science background. The book argues that 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 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. The author points out that as a method of attacking NLP problems, the statistical approach has several advantages. It is grounded in real text and therefore produces usable results.
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