Improving Pronoun Resolution by Incorporating Coreferential Information of Candidates

Improving Pronoun Resolution by Incorporating Coreferential Information of Candidates Xiaofeng Yang†‡ Jian Su† Guodong Zhou† Chew Lim Tan‡ †Institute for Infocomm Research 21 Heng Mui Keng Terrace, Singapore, 119613 {xiaofengy,sujian,zhougd} @i2r.a-star.edu.sg ‡ Department of Computer Science National University of Singapore, Singapore, 117543 {yangxiao,tancl}@comp.nus.edu.sg Abstract Coreferential information of a candidate, such as the properties of its antecedents, is important for pronoun resolution because it reflects the salience of the candidate in the local discourse. Such information, however, is usually ignored in previous learning-based systems. In this paper we present a trainable model which incorporates coreferential information of candidates into pro- noun resolution. Preliminary experiments show that our model will boost the resolution perfor- mance given the right antecedents of the can- didates. We further discuss how to apply our model in real resolution where the antecedents of the candidate are found by a separate noun phrase resolution module. The experimental re- sults show that our model still achieves better performance than the baseline. 1 Introduction In recent years, supervised machine learning ap- proaches have been widely explored in refer- ence resolution and achieved considerable suc- cess (Ge et al., 1998; Soon et al., 2001; Ng and Cardie, 2002; Strube and Muller, 2003; Yang et al., 2003). Most learning-based pronoun res- olution systems determine the reference rela- tionship between an anaphor and its antecedent candidate only from the properties of the pair. The knowledge about the context of anaphor and antecedent is nevertheless ignored. How- ever, research in centering theory (Sidner, 1981; Grosz et al., 1983; Grosz et al., 1995; Tetreault, 2001) has revealed that the local focusing (or centering) also has a great effect on the pro- cessing of pronominal expressions. The choices of the antecedents of pronouns usually depend on the center of attention throughout the local discourse segment (Mitkov, 1999). To determine the salience of a candidate in the local context, we may need to check the coreferential information of the candidate, such as the existence and properties of its an- tecedents. In fact, such information has been used for pronoun resolution in many heuristic- based systems. The S-List model (Strube, 1998), for example, assumes that a co-referring candidate is a hearer-old discourse entity and is preferred to other hearer-new candidates. In the algorithms based on the centering the- ory (Brennan et al., 1987; Grosz et al., 1995), if a candidate and its antecedent are the backward- looking centers of two subsequent utterances re- spectively, the candidate would be the most pre- ferred since the CONTINUE transition is al- ways ranked higher than SHIFT or RETAIN. In this paper, we present a supervised learning-based pronoun resolution system which incorporates coreferential information of candi- dates in a trainable model. For each candi- date, we take into consideration the properties of its antecedents in terms of features (hence- forth backward features), and use the supervised learning method to explore their influences on pronoun resolution. In the study, we start our exploration on the capability of the model by applying it in …

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