A Mention-Synchronous Coreference Resolution Algorithm Based On the Bell Tree

A Mention-Synchronous Coreference Resolution Algorithm Based on the Bell Tree Xiaoqiang Luo and Abe Ittycheriah Hongyan Jing and Nanda Kambhatla and Salim Roukos 1101 Kitchawan Road Yorktown Heights, NY 10598, U.S.A. {xiaoluo,abei,hjing,nanda,roukos}@us.ibm.com Abstract This paper proposes a new approach for coreference resolution which uses the Bell tree to represent the search space and casts the coreference resolution problem as finding the best path from the root of the Bell tree to the leaf nodes. A Maximum Entropy model is used to rank these paths. The coreference performance on the 2002 and 2003 Auto- matic Content Extraction (ACE) data will be reported. We also train a coreference system using the MUC6 data and competitive results are obtained. 1 Introduction In this paper, we will adopt the terminologies used in the Automatic Content Extraction (ACE) task (NIST, 2003). Coreference resolution in this context is defined as partitioning mentions into entities. A mention is an instance of reference to an object, and the collection of mentions referring to the same object in a document form an entity. For example, in the following sentence, mentions are underlined: “The American Medical Association voted yesterday to install the heir apparent as its president-elect, rejecting a strong, upstart challenge by a District doctor who argued that the nation’s largest physicians’ group needs stronger ethics and new leadership.” “American Medical Association”, “its” and “group” belong to the same entity as they refer to the same ob- ject. Early work of anaphora resolution focuses on find- ing antecedents of pronouns (Hobbs, 1976; Ge et al., 1998; Mitkov, 1998), while recent advances (Soon et al., 2001; Yang et al., 2003; Ng and Cardie, 2002; Itty- cheriah et al., 2003) employ statistical machine learn- ing methods and try to resolve reference among all kinds of noun phrases (NP), be it a name, nominal, or pronominal phrase – which is the scope of this paper as well. One common strategy shared by (Soon et al., 2001; Ng and Cardie, 2002; Ittycheriah et al., 2003) is that a statistical model is trained to measure how likely a pair of mentions corefer; then a greedy procedure is followed to group mentions into entities. While this ap- proach has yielded encouraging results, the way men- tions are linked is arguably suboptimal in that an instant decision is made when considering whether two men- tions are linked or not. In this paper, we propose to use the Bell tree to rep- resent the process of forming entities from mentions. The Bell tree represents the search space of the coref- erence resolution problem – each leaf node corresponds to a possible coreference outcome. We choose to model the process from mentions to entities represented in the Bell tree, and the problem of coreference resolution is cast as finding the “best” path from the root node to leaves. A binary maximum entropy model is trained to compute the linking probability between a partial entity and a mention. The rest of the paper is organized as follows. In Section …

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