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dc.contributor.authorChen, Stanley F.
dc.date.accessioned2015-10-06T18:30:00Z
dc.date.issued1995
dc.identifier.citationChen, Stanley F. Bayesian Grammar Induction for Language Modeling. Harvard Computer Science Group Technical Report TR-01-95.en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:23017264
dc.description.abstractWe 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.en_US
dc.description.sponsorshipEngineering and Applied Sciencesen_US
dc.language.isoen_USen_US
dash.licenseLAA
dc.titleBayesian Grammar Induction for Language Modelingen_US
dc.typeResearch Paper or Reporten_US
dc.description.versionVersion of Recorden_US
dc.date.available2015-10-06T18:30:00Z


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