Bayesian Grammar Induction for Language Modeling

dc.creatorChen, Stanley F.
dc.date1995-05-01
dc.date.accessioned2026-07-07T09:09:47Z
dc.date.available2026-07-07T09:09:47Z
dc.descriptionWe 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 the tasks, the training data is generated by a probabilistic context-free grammar and in both tasks our algorithm outperforms the other techniques. The third task involves naturally-occurring data, and in this task our algorithm does not perform as well as n-gram models but vastly outperforms the Inside-Outside algorithm.
dc.description8 pages, LaTeX, uses aclap.sty
dc.identifierhttps://arxiv.org/abs/cmp-lg/9504034
dc.identifierhttp://arxiv.org/abs/cmp-lg/9504034
dc.identifierProc. 33rd Annual Meeting of the ACL, p. 228-235, Cambridge, MA 1995
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/151161
dc.subjectComputation and Language
dc.titleBayesian Grammar Induction for Language Modeling
dc.typetext

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