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We develop the Syntactic Topic Model (STM), a nonparametric Bayesian model of parsed documents. The STM generates words that are both thematically and syntactically constrained, which combines the semantic insights of topic models with the syntactic information available from parse trees. Each word of a sentence is generated by a distribution that combines document-specific topic weights and parse-tree specific syntactic transitions. Words are assumed generated in an order that respects the parse tree. We derive an approximate posterior inference method based on variational methods for hierarchical Dirichlet processes, and we report qualitative and quantitative results on both synthetic data and hand-parsed documents.
Author Information
Jordan Boyd-Graber (University of Maryland)
David Blei (Columbia University)
Related Events (a corresponding poster, oral, or spotlight)
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2008 Poster: Syntactic Topic Models »
Tue. Dec 9th through Mon the 8th Room
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