Paper: Coreference Resolution Using Competition Learning Approach

ACL ID P03-1023
Title Coreference Resolution Using Competition Learning Approach
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2003

In this paper we propose a competition learning approach to coreference resolu- tion. Traditionally, supervised machine learning approaches adopt the single- candidate model. Nevertheless the prefer- ence relationship between the antecedent candidates cannot be determined accu- rately in this model. By contrast, our ap- proach adopts a twin-candidate learning model. Such a model can present the competition criterion for antecedent can- didates reliably, and ensure that the most preferred candidate is selected. Further- more, 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 single- candidate model.