Paper: Sparse Multi-Scale Grammars for Discriminative Latent Variable Parsing

ACL ID D08-1091
Title Sparse Multi-Scale Grammars for Discriminative Latent Variable Parsing
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008
Authors

We present a discriminative, latent variable approach to syntactic parsing in which rules exist at multiple scales of refinement. The model is formally a latent variable CRF gram- mar over trees, learned by iteratively splitting grammar productions (not categories). Dif- ferent regions of the grammar are refined to different degrees, yielding grammars which are three orders of magnitude smaller than the single-scale baseline and 20 times smaller than the split-and-merge grammars of Petrov et al. (2006). In addition, our discriminative approach integrally admits features beyond lo- cal tree configurations. We present a multi- scale training method along with an efficient CKY-style dynamic program. On a variety of domains and languages, this method produces the best published parsing accurac...