Syntactic Topic Models

Part of Advances in Neural Information Processing Systems 21 (NIPS 2008)

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Jordan Boyd-graber, David Blei


We develop \name\ (STM), a nonparametric Bayesian model of parsed documents. \Shortname\ 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.