Paper: Parsing with Compositional Vector Grammars

ACL ID P13-1045
Title Parsing with Compositional Vector Grammars
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013

Natural language parsing has typically been done with small sets of discrete cat- egories such as NP and VP, but this rep- resentation does not capture the full syn- tactic nor semantic richness of linguistic phrases, and attempts to improve on this by lexicalizing phrases or splitting cate- gories only partly address the problem at the cost of huge feature spaces and sparse- ness. Instead, we introduce a Compo- sitional Vector Grammar (CVG), which combines PCFGs with a syntactically un- tied recursive neural network that learns syntactico-semantic, compositional vector representations. The CVG improves the PCFG of the Stanford Parser by 3.8% to obtain an F1 score of 90.4%. It is fast to train and implemented approximately as an efficient reranker it is about 20% faster than the current St...