Coreference Resolution Based on Head Match
Ting Liu · Zhongwen xinxi xuebao · 2011
Coreference Resolution is one of the core issues in Natural Language Processing.Based on flat features for traditional machine learning method,we propose a new method for exploiting information of the head.Firstly,we introduce an instance-matching algorithm based on simple flat features for coreference resolution.With such instance-matching algorithm,we introduce the head string of antecedent and anaphora as new feature,and propose a competition mode to integrate the head-string feature into instance-matching.Compared to other traditional machine learning methods which just consider flat features,our method can fully exploit the feature information for each training instance and the fusion of head string feature produces more accurate result.