tailieunhanh - Báo cáo khoa học: "An Entity-Mention Model for Coreference Resolution with Inductive Logic Programming"

The traditional mention-pair model for coreference 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 performs coreference resolution at an entity level. The model adopts the Inductive Logic Programming (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 coreference decision. The evaluation on the ACE data set shows that the ILP based entity-mention model is effective for the. | An Entity-Mention Model for Coreference Resolution with Inductive Logic Programming Xiaofeng Yang1 Jian Su1 Jun Lang2 Chew Lim Tan3 Ting Liu2 Sheng Li2 institute for Infocomm Research 2Harbin Institute of Technology xiaofengy sujian @ bill_lang tliu @ lisheng@hit. 3National University of Singapore tancl@ Abstract The traditional mention-pair model for coreference 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 performs coreference resolution at an entity level. The model adopts the Inductive Logic Programming 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 coreference decision. The evaluation on the ACE data set shows that the ILP based entity-mention model is effective for the coreference resolution task. 1 Introduction Coreference resolution is the process of linking multiple 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 . 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 information beyond mention pairs is ignored for training and testing. As an individual mention usually lacks adequate 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 .