Paper: Unsupervised Induction of Tree Substitution Grammars for Dependency Parsing

ACL ID D10-1117
Title Unsupervised Induction of Tree Substitution Grammars for Dependency Parsing
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010
Authors

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 depen- dency grammars, however the models em- ployed are overly simplistic, particularly in comparison to supervised parsing models. In this paper we present an approach to depen- dency grammar induction using tree substi- tution grammar which is capable of learn- ing large dependency fragments and thereby better modelling the text. We define a hi- erarchical non-parametric Pitman-Yor Process prior which biases towards a small grammar with simple productions. This approach sig- nificantly improves the state-of-the-art, when measured by head attachment accuracy.