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Bayesian Finite Mixture Models for Probabilistic Context-Free Grammars

  • Philip L. H. YuEmail author
  • Yaohua Tang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9041)

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.

Keywords

Bayesian Finite Mixture Model Phrase Parsing MCMC 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Statistics & Actuarial ScienceThe University of Hong KongPok Fu LamHong Kong, China

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