Bayesian finite mixture models for probabilistic context-free grammars

Leung Ho Philip YU, Yaohua TANG

Research output: Chapter in Book/Report/Conference proceedingChapters

Abstract

Instead of using a common PCFG to parse all texts, we present an efficient generative probabilistic model for the probabilistic context-free grammars(PCFGs) based on the Bayesian finite mixture model, where we assume that there are several PCFGs and each of these PCFGs share the same CFG but with different rule probabilities. Sentences of the same article in the corpus are generated from a common multinomial distribution over these PCFGs. We derive a Markov chain Monte Carlo algorithm for this model. In the experiments, our multi-grammar model outperforms both single grammar model and Inside-Outside algorithm. Copyright © 2015 Springer International Publishing Switzerland.
Original languageEnglish
Title of host publicationComputational linguistics and intelligent text processing: 16th International Conference, CICLing 2015, Cairo, Egypt, April 14-20, 2015, proceedings, part I
EditorsAlexander GELBUKH
Place of PublicationCham
PublisherSpringer
Pages201-212
ISBN (Electronic)9783319181110
ISBN (Print)9783319181103
DOIs
Publication statusPublished - 2015

Citation

Yu, P. L. H., & Tang, Y. (2015). Bayesian finite mixture models for probabilistic context-free grammars. In A. Gelbukh (Ed.), Computational linguistics and intelligent text processing: 16th International Conference, CICLing 2015, Cairo, Egypt, April 14-20, 2015, proceedings, part I (pp. 201-212). Cham: Springer.

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