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- W2049901611 abstract "We 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, we find the closest n-gram distribution to the static n-gram distribution (using the discrimination information distance measure) and 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." @default.
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- W2049901611 date "1992-01-01" @default.
- W2049901611 modified "2023-09-23" @default.
- W2049901611 title "Adaptive language modeling using minimum discriminant estimation" @default.
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- W2049901611 doi "https://doi.org/10.3115/1075527.1075551" @default.
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