Paper: Extremely Lexicalized Models For Accurate And Fast HPSG Parsing

ACL ID W06-1619
Title Extremely Lexicalized Models For Accurate And Fast HPSG Parsing
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006
Authors

This paper describes an extremely lexi- calized probabilistic model for fast and accurate HPSG parsing. In this model, the probabilities of parse trees are de- fined with only the probabilities of select- ing lexical entries. The proposed model is very simple, and experiments revealed that the implemented parser runs around four times faster than the previous model and that the proposed model has a high accuracy comparable to that of the previ- ous model for probabilistic HPSG, which is defined over phrase structures. We also developed a hybrid of our probabilis- tic model and the conventional phrase- structure-based model. The hybrid model is not only significantly faster but also sig- nificantly more accurate by two points of precision and recall compared to the pre- vious model.