Kernel-Based Pronoun Resolution with Structured Syntactic Knowledge

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 41–48, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Kernel-Based Pronoun Resolution with Structured Syntactic Knowledge Xiaofeng Yang† Jian Su† Chew Lim Tan‡ †Institute for Infocomm Research 21 Heng Mui Keng Terrace, Singapore, 119613 {xiaofengy,sujian}@i2r.a-star.edu.sg ‡ Department of Computer Science National University of Singapore, Singapore, 117543 tancl@comp.nus.edu.sg Abstract Syntactic knowledge is important for pro- noun resolution. Traditionally, the syntac- tic information for pronoun resolution is represented in terms of features that have to be selected and defined heuristically. In the paper, we propose a kernel-based method that can automatically mine the syntactic information from the parse trees for pronoun resolution. Specifically, we utilize the parse trees directly as a struc- tured feature and apply kernel functions to this feature, as well as other normal fea- tures, to learn the resolution classifier. In this way, our approach avoids the efforts of decoding the parse trees into the set of flat syntactic features. The experimental results show that our approach can bring significant performance improvement and is reliably effective for the pronoun reso- lution task. 1 Introduction Pronoun resolution is the task of finding the cor- rect antecedent for a given pronominal anaphor in a document. Prior studies have suggested that syntactic knowledge plays an important role in pronoun resolution. For a practical pronoun res- olution system, the syntactic knowledge usually comes from the parse trees of the text. The is- sue that arises is how to effectively incorporate the syntactic information embedded in the parse trees to help resolution. One common solution seen in previous work is to define a set of features that rep- resent particular syntactic knowledge, such as the grammatical role of the antecedent candidates, the governing relations between the candidate and the pronoun, and so on. These features are calculated by mining the parse trees, and then could be used for resolution by using manually designed rules (Lappin and Leass, 1994; Kennedy and Boguraev, 1996; Mitkov, 1998), or using machine-learning methods (Aone and Bennett, 1995; Yang et al., 2004; Luo and Zitouni, 2005). However, such a solution has its limitation. The syntactic features have to be selected and defined manually, usually by linguistic intuition. Unfor- tunately, what kinds of syntactic information are effective for pronoun resolution still remains an open question in this research community. The heuristically selected feature set may be insuffi- cient to represent all the information necessary for pronoun resolution contained in the parse trees. In this paper we will explore how to utilize the syntactic parse trees to help learning-based pro- noun resolution. Specifically, we directly utilize the parse trees as a structured feature, and then use a kernel-based method to automatically mine the knowledge embedded in the parse trees. The struc- tured syntactic feature, together with other nor- mal features, is incorporated in a trainable model based on Support Vector Machine (SVM) (Vapnik, 1995) to learn the decision classifier for resolution. Indeed, using kernel methods …

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