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Statistical Language Learning

Statistical Language Learning

Statistical Language Learning
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Statistical Language Learning Paperback - 1996

by Charniak, Eugene

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MIT Press. Used - Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.
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Details

  • Title Statistical Language Learning
  • Author Charniak, Eugene
  • Binding Paperback
  • Edition Reprint
  • Condition Used - Good
  • Pages 190
  • Volumes 1
  • Language ENG
  • Publisher MIT Press, Cambridge, MA, and London
  • Publication date September 1, 1996
  • Bookseller's Inventory # 7518913-6
  • ISBN 9780262531412 / 0262531410
  • Weight 0.71 lbs (0.32 kg)
  • Dimensions 9.02 x 6.08 x 0.45 in (22.91 x 15.44 x 1.14 cm)
  • Age range 18 to UP years
  • Grade levels 13 - UP
  • Category Language Arts / Linguistics / Literacy
  • Library of Congress Catalogue Number 93-28080
  • Dewey Decimal Code 410
  • Quantity available 1

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Reader reviews for Statistical Language Learning

From the publisher

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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