Simple, Accurate Parsing with an All-Fragments Grammar

We present a simple but accurate parser which exploits both large tree fragments and symbol refinement. We parse with all fragments of the training set, in contrast to much recent work on tree selection in data-oriented parsing and treesubstitution grammar learning. We require only simple, deterministic grammar symbol refinement, in contrast to recent work on latent symbol refinement. Moreover, our parser requires no explicit lexicon machinery, instead parsing input sentences as character streams. Despite its simplicity, our parser achieves accuracies of over 88% F1 on the standard English WSJ task, which is competitive with substantially more complicated state-of-theart lexicalized and latent-variable parsers. Additional specific contributions center on making implicit all-fragments parsing efficient, including a coarse-to-fine inference scheme and a new graph encoding.

• Type: Journal
• Year: 2010
• Where: ACL
• Authors: Mohit Bansal, Dan Klein
• Keywords: ACL 2010, Computational Linguistics, Grammar Symbol Refinement, Parsers, Symbol Refinement