Capturing Salience with a Trainable Cache Model for Zero-anaphora Resolution

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 647–655, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Capturing Salience with a Trainable Cache Model for Zero-anaphora Resolution Ryu Iida Department of Computer Science Tokyo Institute of Technology 2-12-1, ˆOokayama, Meguro, Tokyo 152-8552, Japan ryu-i@cl.cs.titech.ac.jp Kentaro Inui Yuji Matsumoto Graduate School of Information Science Nara Institute of Science and Technology 8916-5, Takayama, Ikoma Nara 630-0192, Japan {inui,matsu}@is.naist.jp Abstract This paper explores how to apply the notion of caching introduced by Walker (1996) to the task of zero-anaphora resolution. We propose a machine learning-based imple- mentation of a cache model to reduce the computational cost of identifying an an- tecedent. Our empirical evaluation with Japanese newspaper articles shows that the number of candidate antecedents for each zero-pronoun can be dramatically reduced while preserving the accuracy of resolving it. 1 Introduction There have been recently increasing concerns with the need for anaphora resolution to make NLP applications such as IE and MT more reli- able. In particular, for languages such as Japanese, anaphora resolution is crucial for resolving a phrase in a text to its referent since phrases, es- pecially nominative arguments of predicates, are frequently omitted by anaphoric functions in dis- course (Iida et al., 2007b). Many researchers have recently explored ma- chine learning-based methods using considerable amounts of annotated data provided by, for exam- ple, the Message Understanding Conference and Automatic Context Extraction programs (Soon et al., 2001; Ng and Cardie, 2002; Yang et al., 2008; McCallum and Wellner, 2003, etc.). These meth- ods reach a level comparable to or better than the state-of-the-art rule-based systems (e.g. Baldwin (1995)) by recasting the task of anaphora resolution into classification or clustering problems. How- ever, such approaches tend to disregard theoretical findings from discourse theories, such as Center- ing Theory (Grosz et al., 1995). Therefore, one of the challenging issues in this area is to incorporate such findings from linguistic theories into machine learning-based approaches. A typical machine learning-based approach to zero-anaphora resolution searches for an an- tecedent in the set of candidates appearing in all the preceding contexts. However, computational time makes this approach largely infeasible for long texts. An alternative approach is to heuristi- cally limit the search space (e.g. the system deals with candidates only occurring in the N previous sentences). Various research such as Yang et al. (2008) has adopted this approach, but it also leads to problems when an antecedent is located far from its anaphor, causing it to be excluded from target candidate antecedents. On the other hand, rule-based methods derived from theoretical background such as Centering Theory (Grosz et al., 1995) only deal with the salient discourse entities at each point of the dis- course status. By incrementally updating the dis- course status, the set of candidates in question is automatically limited. Although these meth- ods have a theoretical advantage, they have a serious drawback in that Centering Theory only retains information about the previous sentence. …

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