Paper: Nonparametric Bayesian Inference and Efficient Parsing for Tree-adjoining Grammars

ACL ID P13-2106
Title Nonparametric Bayesian Inference and Efficient Parsing for Tree-adjoining Grammars
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2013
Authors

In the line of research extending statis- tical parsing to more expressive gram- mar formalisms, we demonstrate for the first time the use of tree-adjoining gram- mars (TAG). We present a Bayesian non- parametric model for estimating a proba- bilistic TAG from a parsed corpus, along with novel block sampling methods and approximation transformations for TAG that allow efficient parsing. Our work shows performance improvements on the Penn Treebank and finds more compact yet linguistically rich representations of the data, but more importantly provides techniques in grammar transformation and statistical inference that make practical the use of these more expressive systems, thereby enabling further experimentation along these lines.