Abstract
Tree substitution grammars (TSGs) are a compelling alternative to context-free grammars for modelling syntax. However, many popular techniques for estimating weighted TSGs (under the moniker of Data Oriented Parsing) suffer from the problems of inconsistency and over-fitting. We present a theoretically principled model which solves these problems using a Bayesian non-parametric formulation. Our model learns compact and simple grammars, uncovering latent linguistic structures (e.g., verb subcategorisation), and in doing so far out-performs a standard PCFG. © 2009 Association for Computational Linguistics.
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CITATION STYLE
Cohn, T., Goldwater, S., & Blunsom, P. (2009). Inducing compact but accurate tree-substitution grammars. In NAACL HLT 2009 - Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Conference (pp. 548–556). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1620754.1620834
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