PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Inducing Compact but Accurate Tree-Substitution Grammars
Trevor Cohn, Phil Blunsom and Sharon Goldwater
In: NAACL 2009, 31 May - 5 June 2009, Boulder, USA.

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 us- ing a Bayesian non-parametric formulation. Our model learns compact and simple gram- mars, uncovering latent linguistic structures (e.g., verb subcategorisation), and in doing so far out-performs a standard PCFG.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Natural Language Processing
ID Code:6749
Deposited By:Phil Blunsom
Deposited On:08 March 2010