An Entity-Mention Model for Coreference Resolution with Inductive Logic Programming

Proceedings of ACL-08: HLT, pages 843–851, Columbus, Ohio, USA, June 2008. c⃝2008 Association for Computational Linguistics An Entity-Mention Model for Coreference Resolution with Inductive Logic Programming Xiaofeng Yang1 Jian Su1 Jun Lang2 Chew Lim Tan3 Ting Liu2 Sheng Li2 1Institute for Infocomm Research {xiaofengy,sujian}@i2r.a-star.edu.sg 2Harbin Institute of Technology {bill lang,tliu}@ir.hit.edu.cn lisheng@hit.edu.cn 3National University of Singapore, tancl@comp.nus.edu.sg Abstract The traditional mention-pair model for coref- erence resolution cannot capture information beyond mention pairs for both learning and testing. To deal with this problem, we present an expressive entity-mention model that per- forms coreference resolution at an entity level. The model adopts the Inductive Logic Pro- gramming (ILP) algorithm, which provides a relational way to organize different knowledge of entities and mentions. The solution can explicitly express relations between an entity and the contained mentions, and automatically learn first-order rules important for corefer- ence decision. The evaluation on the ACE data set shows that the ILP based entity-mention model is effective for the coreference resolu- tion task. 1 Introduction Coreference resolution is the process of linking mul- tiple mentions that refer to the same entity. Most of previous work adopts the mention-pair model, which recasts coreference resolution to a binary classification problem of determining whether or not two mentions in a document are co-referring (e.g. Aone and Bennett (1995); McCarthy and Lehnert (1995); Soon et al. (2001); Ng and Cardie (2002)). Although having achieved reasonable success, the mention-pair model has a limitation that informa- tion beyond mention pairs is ignored for training and testing. As an individual mention usually lacks ad- equate descriptive information of the referred entity, it is often difficult to judge whether or not two men- tions are talking about the same entity simply from the pair alone. An alternative learning model that can overcome this problem performs coreference resolution based on entity-mention pairs (Luo et al., 2004; Yang et al., 2004b). Compared with the traditional mention- pair counterpart, the entity-mention model aims to make coreference decision at an entity level. Classi- fication is done to determine whether a mention is a referent of a partially found entity. A mention to be resolved (called active mention henceforth) is linked to an appropriate entity chain (if any), based on clas- sification results. One problem that arises with the entity-mention model is how to represent the knowledge related to an entity. In a document, an entity may have more than one mention. It is impractical to enumerate all the mentions in an entity and record their informa- tion in a single feature vector, as it would make the feature space too large. Even worse, the number of mentions in an entity is not fixed, which would re- sult in variant-length feature vectors and make trou- ble for normal machine learning algorithms. A solu- tion seen in previous work (Luo et al., 2004; Culotta et al., 2007) is to design a set of first-order features summarizing the information of the mentions in an entity, for example, “whether the entity has any men- tion …

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