Extremely Lexicalized Models for Accurate and Fast HPSG Parsing

Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), pages 155–163, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Extremely Lexicalized Models for Accurate and Fast HPSG Parsing Takashi Ninomiya Information Technology Center University of Tokyo Takuya Matsuzaki Department of Computer Science University of Tokyo Yoshimasa Tsuruoka School of Informatics University of Manchester Yusuke Miyao Department of Computer Science University of Tokyo Jun’ichi Tsujii Department of Computer Science, University of Tokyo School of Informatics, University of Manchester SORST, Japan Science and Technology Agency Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-0033, Japan {ninomi, matuzaki, tsuruoka, yusuke, tsujii}@is.s.u-tokyo.ac.jp Abstract 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. 1 Introduction For the last decade, accurate and wide-coverage parsing for real-world text has been intensively and extensively pursued. In most of state-of-the- art parsers, probabilistic events are defined over phrase structures because phrase structures are supposed to dominate syntactic configurations of sentences. For example, probabilities were de- fined over grammar rules in probabilistic CFG (Collins, 1999; Klein and Manning, 2003; Char- niak and Johnson, 2005) or over complex phrase structures of head-driven phrase structure gram- mar (HPSG) or combinatory categorial grammar (CCG) (Clark and Curran, 2004b; Malouf and van Noord, 2004; Miyao and Tsujii, 2005). Although these studies vary in the design of the probabilistic models, the fundamental conception of probabilis- tic modeling is intended to capture characteristics of phrase structures or grammar rules. Although lexical information, such as head words, is known to significantly improve the parsing accuracy, it was also used to augment information on phrase structures. Another interesting approach to this problem was using supertagging (Clark and Curran, 2004b; Clark and Curran, 2004a; Wang and Harper, 2004; Nasr and Rambow, 2004), which was originally developed for lexicalized tree adjoining grammars (LTAG) (Bangalore and Joshi, 1999). Supertag- ging is a process where words in an input sen- tence are tagged with ‘supertags,’ which are lex- ical entries in lexicalized grammars, e.g., elemen- tary trees in LTAG, lexical categories in CCG, and lexical entries in HPSG. Supertagging was, in the first place, a technique to reduce the cost of parsing with lexicalized grammars; ambiguity in assigning lexical entries to words is reduced by the light-weight process of supertagging be- fore the heavy process of parsing. Bangalore and Joshi (1999) claimed that if …

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