Nonparametric Bayesian Inference and Cubic-time Parsing for Tree-adjoining Grammars
The 51st Annual Meeting of the Association for Computational Linguistics - Short Papers (ACL Short Papers 2013)
Sofia, Bulgaria, August 4-9, 2013
In the line of research extending statistical parsing to more expressive grammar formalisms, we demonstrate for the first time the use of tree-adjoining grammars (TAG). We present a Bayesian nonparametric model for estimating a probabilistic 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.
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