Logistic Normal Priors for Unsupervised Probabilistic Grammar Induction

S. B. Cohen, K. Gimpel, N. A. Smith

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We explore a new Bayesian model for probabilistic grammars, a family of distributions over discrete structures that includes hidden Markov models and probabilistic context-free grammars. Our model extends the correlated topic model framework to probabilistic grammars, exploiting the logistic normal distribution as a prior over the grammar parameters. We derive a variational EM algorithm for that model, and then experiment with the task of unsupervised grammar induction for natural language dependency parsing. We show that our model achieves superior results over previous models that use different priors.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 21 (NIPS 2008)
Number of pages8
Publication statusPublished - 2009


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