Large Scale Distributed Syntactic, Semantic and Lexical Language Models

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United States of America Patent

APP PUB NO 20130325436A1
SERIAL NO

13482529

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Abstract

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A composite language model may include a composite word predictor. The composite word predictor may include a first language model and a second language model that are combined according to a directed Markov random field. The composite word predictor can predict a next word based upon a first set of contexts and a second set of contexts. The first language model may include a first word predictor that is dependent upon the first set of contexts. The second language model may include a second word predictor that is dependent upon the second set of contexts. Composite model parameters can be determined by multiple iterations of a convergent N-best list approximate Expectation-Maximization algorithm and a follow-up Expectation-Maximization algorithm applied in sequence, wherein the convergent N-best list approximate Expectation-Maximization algorithm and the follow-up Expectation-Maximization algorithm extracts the first set of contexts and the second set of contexts from a training corpus.

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Patent Owner(s)

Patent OwnerAddress
WRIGHT STATE UNIVERSITY3640 COLONEL GLENN HWY DAYTON OH 45435

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Tan, Ming Fairborn, US 65 1033
Wang, Shaojun Centerville, US 21 298

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