Bayesian Grammar Induction for Language Modeling

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Bayesian Grammar Induction for Language Modeling

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Title: Bayesian Grammar Induction for Language Modeling
Author: Chen, Stanley F.
Citation: Chen, Stanley F. Bayesian Grammar Induction for Language Modeling. Harvard Computer Science Group Technical Report TR-01-95.
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Abstract: We describe a corpus-based induction algorithm for probabilistic context-free grammars. The algorithm employs a greedy heuristic search within a Bayesian framework, and a post-pass using the Inside-Outside algorithm. We compare the performance of our algorithm to n-gram models and the Inside-Outside algorithm in three language modeling tasks. In two of these domains, our algorithm outperforms these other techniques, marking the first time a grammar-based language model has surpassed n-gram modeling in a task of at least moderate size.
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:23017264
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