FEILIANG REN
Natural Language Processing Lab, Institute of Computer Software and Theory, Northeastern University, Shenyang, 110004, China
JINGBO ZHU
Natural Language Processing Lab, Institute of Computer Software and Theory, Northeastern University, Shenyang, 110004, China
HUIZHEN WANG
Natural Language Processing Lab, Institute of Computer Software and Theory, Northeastern University, Shenyang, 110004, China
TONG XIAO
Natural Language Processing Lab, Institute of Computer Software and Theory, Northeastern University, Shenyang, 110004, China

We present a machine learning approach for coreference resolution of noun phrases. In our method, we use CRFs as a basic training model, and use active learning method to generate combined features so as to use existing features more effectively. We also propose a novel clustering algorithm which uses both linguistic knowledge and statistical knowledge. We build a coreference resolution system based on the proposed method and evaluate its performance from three aspects: the contributions of active learning; the effects of different clustering algorithms; and the resolution performance of different kinds of NPs. Experimental results show that additional performance gain can be obtained by using active learning method; clustering algorithm has a great effect on coreference resolution's performance and our clustering algorithm is very effective; and the key of coreference resolution is to improve the performance of the normal noun's resolution, especially the pronoun's resolution.

Coreference resolution; Active learning; Clustering algorithm; CRFs