Date of Original Version
Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Copyright 2010 ACL
Abstract or Description
Adaptor grammars extend probabilistic context-free grammars to define prior distributions over trees with “rich get richer” dynamics. Inference for adaptor grammars seeks to find parse trees for raw text. This paper describes a variational inference algorithm for adaptor grammars, providing an alternative to Markov chain Monte Carlo methods. To derive this method, we develop a stick-breaking representation of adaptor grammars, a representation that enables us to define adaptor grammars with recursion. We report experimental results on a word segmentation task, showing that variational inference performs comparably to MCMC. Further, we show a significant speed-up when parallelizing the algorithm. Finally, we report promising results for a new application for adaptor grammars, dependency grammar induction.
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Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 564-572.