Coreference Resolution Using Competition Learning Approach

In this paper we propose a competition learning approach to coreference resolution. Traditionally, supervised machine learning approaches adopt the singlecandidate model. Nevertheless the preference relationship between the antecedent candidates cannot be determined accurately in this model. By contrast, our approach adopts a twin-candidate learning model. Such a model can present the competition criterion for antecedent candidates reliably, and ensure that the most preferred candidate is selected. Furthermore, our approach applies a candidate filter to reduce the computational cost and data noises during training and resolution. The experimental results on MUC-6 and MUC-7 data set show that our approach can outperform those based on the singlecandidate model.

• Type: Conference
• Year: 2003
• Where: ACL
• Authors: Xiaofeng Yang, Guodong Zhou, Jian Su, Chew Lim Tan
• Keywords: ACL 2003, ACL 2007, Antecedent Candidates, Competition Learning Approach, Singlecandidate Model