Description - Statistical Language Learning by Eugene Charniak
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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(224mm x 150mm x 10mm)
Publisher: MIT Press Ltd
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