Bayesian Learning of a Tree Substitution Grammar

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

• Type: Journal
• Year: 2009
• Where: ACL
• Authors: Matt Post, Daniel Gildea
• Keywords: ACL 2009, Computational Linguistics, Context-free Grammars, Grammars, Tree Substitution