An NP-Cluster Based Approach to Coreference Resolution

An NP-Cluster Based Approach to Coreference Resolution 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 Traditionally, coreference resolution is done by mining the reference relationships be- tween NP pairs. However, an individual NP usually lacks adequate description informa- tion of its referred entity. In this paper, we propose a supervised learning-based ap- proach which does coreference resolution by exploring the relationships between NPs and coreferential clusters. Compared with indi- vidual NPs, coreferential clusters could pro- vide richer information of the entities for bet- ter rules learning and reference determina- tion. The evaluation done on MEDLINE data set shows that our approach outper- forms the baseline NP-NP based approach in both recall and precision. 1 Introduction Coreference resolution is the process of linking as a cluster1 multiple expressions which refer to the same entities in a document. In recent years, supervised machine learning approaches have been applied to this problem and achieved considerable success (e.g. Aone and Bennett (1995); McCarthy and Lehnert (1995); Soon et al. (2001); Ng and Cardie (2002b)). The main idea of most supervised learning approaches is to recast this task as a binary classification prob- lem. Specifically, a classifier is learned and then used to determine whether or not two NPs in a document are co-referring. Clusters are formed by linking coreferential NP pairs according to a certain selection strategy. In this way, the identi- fication of coreferential clusters in text is reduced to the identification of coreferential NP pairs. One problem of such reduction, however, is that the individual NP usually lacks ade- quate descriptive information of its referred en- tity. Consequently, it is often difficult to judge whether or not two NPs are talking about the 1In this paper the term “cluster” can be interchange- ably used as “chain”, while the former better emphasizes the equivalence property of coreference relationship. same entity simply from the properties of the pair alone. As an example, consider the pair of a non-pronoun and its pronominal antecedent can- didate. The pronoun itself gives few clues for the reference determination. Using such NP pairs would have a negative influence for rules learn- ing and subsequent resolution. So far, several efforts (Harabagiu et al., 2001; Ng and Cardie, 2002a; Ng and Cardie, 2002b) have attempted to address this problem by discarding the “hard” pairs and select only those confident ones from the NP-pair pool. Nevertheless, this eliminat- ing strategy still can not guarantee that the NPs in “confident” pairs bear necessary description information of their referents. In this paper, we present a supervised learning-based approach to coreference resolu- tion. Rather than attempting to mine the ref- erence relationships between NP pairs, our ap- proach does resolution by determining the links of NPs to the existing coreferential clusters. In our approach, a classifier is trained on the in- stances formed by an NP and one …

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