Adaptive language modeling using minimum discriminant estimation


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    Views: (2001)   Date: (Publication Date: 23-26 Mar 19...)   Pages: ()
  • Author:  Della Pietra  S. Della Pietra  V. Mercer  R.L. Roukos  S. IBM Thomas J. Watson Res. Center  Yorktown Heights  NY;  

  • Abstract:  Abstract The authors present an algorithm to adapt a n-gram language model to a document as it is dictated. The observed partial document is used to estimate a unigram distribution for the words that already occurred. Then, they find the closest n-gram distribution to the static n-gram distribution (using the discrimination information distance measure) that satisfies the marginal constraints derived from the document. The resulting minimum discrimination information model results in a perplexity of 208 instead of 290 for the static trigram model on a document of 321 words

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