Date of Original Version

6-2010

Type

Conference Proceeding

Journal Title

Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

First Page

564

Last Page

572

Rights Management

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.

Creative Commons License

Creative Commons Attribution-Noncommercial-Share Alike 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

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Published In

Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 564-572.