Probabilistic Disambiguation Models for Wide-Coverage HPSG Parsing

Proceedings of the 43rd Annual Meeting of the ACL, pages 83–90, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Probabilistic disambiguation models for wide-coverage HPSG parsing Yusuke Miyao Department of Computer Science University of Tokyo Hongo 7-3-1, Bunkyo-ku, Tokyo, Japan yusuke@is.s.u-tokyo.ac.jp Jun’ichi Tsujii Department of Computer Science University of Tokyo Hongo 7-3-1, Bunkyo-ku, Tokyo, Japan CREST, JST tsujii@is.s.u-tokyo.ac.jp Abstract This paper reports the development of log- linear models for the disambiguation in wide-coverage HPSG parsing. The esti- mation of log-linear models requires high computational cost, especially with wide- coverage grammars. Using techniques to reduce the estimation cost, we trained the models using 20 sections of Penn Tree- bank. A series of experiments empiri- cally evaluated the estimation techniques, and also examined the performance of the disambiguation models on the parsing of real-world sentences. 1 Introduction Head-Driven Phrase Structure Grammar (HPSG) (Pollard and Sag, 1994) has been studied extensively from both linguistic and computational points of view. However, despite research on HPSG process- ing efficiency (Oepen et al., 2002a), the application of HPSG parsing is still limited to specific domains and short sentences (Oepen et al., 2002b; Toutanova and Manning, 2002). Scaling up HPSG parsing to assess real-world texts is an emerging research field with both theoretical and practical applications. Recently, a wide-coverage grammar and a large treebank have become available for English HPSG (Miyao et al., 2004). A large treebank can be used as training and test data for statistical models. There- fore, we now have the basis for the development and the evaluation of statistical disambiguation models for wide-coverage HPSG parsing. The aim of this paper is to report the development of log-linear models for the disambiguation in wide- coverage HPSG parsing, and their empirical evalua- tion through the parsing of the Wall Street Journal of Penn Treebank II (Marcus et al., 1994). This is chal- lenging because the estimation of log-linear models is computationally expensive, and we require solu- tions to make the model estimation tractable. We apply two techniques for reducing the training cost. One is the estimation on a packed representation of HPSG parse trees (Section 3). The other is the filter- ing of parse candidates according to a preliminary probability distribution (Section 4). To our knowledge, this work provides the first re- sults of extensive experiments of parsing Penn Tree- bank with a probabilistic HPSG. The results from the Wall Street Journal are significant because the complexity of the sentences is different from that of short sentences. Experiments of the parsing of real- world sentences can properly evaluate the effective- ness and possibility of parsing models for HPSG. 2 Disambiguation models for HPSG Discriminative log-linear models are now becom- ing a de facto standard for probabilistic disambigua- tion models for deep parsing (Johnson et al., 1999; Riezler et al., 2002; Geman and Johnson, 2002; Miyao and Tsujii, 2002; Clark and Curran, 2004b; Kaplan et al., 2004). Previous studies on prob- abilistic models for HPSG (Toutanova and Man- ning, 2002; Baldridge and Osborne, 2003; Malouf …

Content truncated to 500 words