Paper: Bayesian Learning of a Tree Substitution Grammar

ACL ID P09-2012
Title Bayesian Learning of a Tree Substitution Grammar
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2009
Authors

Tree substitution grammars (TSGs) of- fer many advantages over context-free grammars (CFGs), but are hard to learn. Past approaches have resorted to heuris- tics. In this paper, we learn a TSG us- ing Gibbs sampling with a nonparamet- ric prior to control subtree size. The learned grammars perform significantly better than heuristically extracted ones on parsing accuracy.