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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 Automatic Content Extraction (ACE) data will be reported. We also train a coreference system using the MUC6 data and competitive results are obtained. | 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 Automatic 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 object. Early work of anaphora resolution focuses on finding 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 learning 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
