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Adaptive Language Modeling Using the Maximum Entropy Principle
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In: DTIC (1993)
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Abstract:
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 long-distance 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. ; Presented at the Human Language Technology Conference, held in Princeton, NJ, on 21-24 Mar 1993. Published in the Proceedings of the Workshop on Human Language Technology, p108-113, 1993. Sponsored in part by Defense Advanced Research Projects Agency (DARPA).
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Keyword:
*ENTROPY; *LANGUAGE; *ME(MAXIMUM ENTROPY); *ML(MAXIMUM LIKELIHOOD); *MODELS; ALGORITHMS; ITERATIVE ALGORITHMS; Linguistics; MAXIMUM LIKELIHOOD ESTIMATION; STATISTICAL ANALYSIS; SYMPOSIA
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URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA460662
http://www.dtic.mil/docs/citations/ADA460662
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