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Unpublished Paper
Bayesian Modeling of Dependency Trees Using Hierarchical Pitman-Yor Priors
  • Hanna M. Wallach, University of Massachusetts - Amherst
  • Charles Sutton
  • Andrew McCallum
In this paper, we introduce two hierarchical Bayesian dependency parsing models. First, we show that a classic dependency parser can be substantially improved by (a) using a hierarchical Pitman-Yor process prior over the distribution over dependents of a word, and (b) sampling the model hyperparameters. Second, we present a parsing model in which latent state variables mediate the relationships between words and their dependents. The model clusters dependencies into states using a similar approach to that used by Bayesian topic models when clustering words into topics. The inferred states have a syntactic character, and lead to modestly improved parse accuracy when substituted for part-of-speech tags in the parsing model.
Publication Date
This is the pre-published version harvested from CIIR.
Citation Information
Hanna M. Wallach, Charles Sutton and Andrew McCallum. "Bayesian Modeling of Dependency Trees Using Hierarchical Pitman-Yor Priors" (2008)
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