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2003

Coreference Resolution Using Competition Learning Approach

9 years 3 months ago
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.
Xiaofeng Yang, Guodong Zhou, Jian Su, Chew Lim Tan
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where ACL
Authors Xiaofeng Yang, Guodong Zhou, Jian Su, Chew Lim Tan
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