PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Unsupervised Induction of Tree Substitution Grammars for Dependency Parsing
Phil Blunsom and Trevor Cohn
In: Empirical Methods in Natural Language Processing, 9-11 Oct 2010, Boston.


Inducing a grammar directly from text is one of the oldest and most challenging tasks in Computational Linguistics. Significant progress has been made for inducing dependency grammars, however the models employed are overly simplistic, particularly in comparison to supervised parsing models. In this paper we present an approach to dependency grammar induction using tree substitution grammar which is capable of learning large dependency fragments and thereby better modelling the text. We define a hierarchical non-parametric Pitman-Yor Process prior which biases towards a small grammar with simple productions. This approach significantly improves the state-of-the-art, when measured by head attachment accuracy.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:7944
Deposited By:Phil Blunsom
Deposited On:17 March 2011