Extremely lexicalized models for accurate and fast HPSG parsing

Authors:
Takashi Ninomiya;Takuya Matsuzaki;Yoshimasa Tsuruoka;Yusuke Miyao;Jun'ichi Tsujii
Affiliations:
University of Tokyo;University of Tokyo;University of Manchester;University of Tokyo;University of Tokyo and University of Manchester, Hongo, Bunkyo-ku, Tokyo, Japan
Venue:
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Year:
2006

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Abstract

This paper describes an extremely lexicalized probabilistic model for fast and accurate HPSG parsing. In this model, the probabilities of parse trees are defined with only the probabilities of selecting 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 previous model for probabilistic HPSG, which is defined over phrase structures. We also developed a hybrid of our probabilistic model and the conventional phrase-structure-based model. The hybrid model is not only significantly faster but also significantly more accurate by two points of precision and recall compared to the previous model.