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A Maximum Entropy Approach to Adaptive Statistical Language Modeling
 Computer, Speech and Language
, 1996
"... An adaptive statistical languagemodel is described, which successfullyintegrates long distancelinguistic information with other knowledge sources. Most existing statistical language models exploit only the immediate history of a text. To extract information from further back in the document's histor ..."
Abstract

Cited by 241 (11 self)
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An adaptive statistical languagemodel is described, which successfullyintegrates long distancelinguistic information with other knowledge sources. Most existing statistical language models exploit only the immediate history of a text. To extract information from further back in the document's history, we propose and use trigger pairs as the basic information bearing elements. This allows the model to adapt its expectations to the topic of discourse. Next, statistical evidence from multiple sources must be combined. Traditionally, linear interpolation and its variants have been used, but these are shown here to be seriously deficient. Instead, we apply the principle of Maximum Entropy (ME). Each information source gives rise to a set of constraints, to be imposed on the combined estimate. The intersection of these constraints is the set of probability functions which are consistent with all the information sources. The function with the highest entropy within that set is the ME solution...
Adaptive language modeling using the maximum entropy principle.” Human Language Technology
 Proceedings of a Workshop Held at Plainsboro
, 1993
"... We describe our ongoing efforts at adaptive statistical language modeling. Central to our approach is the Maximum Entropy (ME) Principle, allowing us to combine evidence from multiple sources, such as longdistance triggers and conventional short.distance trigrams. Given consistent statistical evide ..."
Abstract

Cited by 37 (5 self)
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We describe our ongoing efforts at adaptive statistical language modeling. Central to our approach is the Maximum Entropy (ME) Principle, allowing us to combine evidence from multiple sources, such as longdistance triggers and conventional short.distance trigrams. Given consistent statistical evidence, a unique ME solution is guaranteed to exist, and an iterative algorithm exists which is guaranteed to converge to it. Among the advantages of this approach are its simplicity, its generality, and its incremental nature. Among its disadvantages are its computational requirements. We describe a succession of ME models, culminating in our current Maximum Likelihood / Maximum Entropy (ML/ME) model. Preliminary results with the latter show a 27 % perplexity reduction as compared to a conventional trigram model. 1. STATE OF THE ART