A New Graph Clustering Algorithm for Chinese Noun Phrase Coreference Resolution
Weiguang Qu · Zhongwen xinxi xuebao · 2007
Coreference resolution plays an important role in natural language processing.Facing the fact that the Chinese training corpus for coreference resolution is heavily lacking,this paper presents a new unsupervised clustering algorithm for noun phrase coreference resolution.In this approach,the problem of coreference resolution is firstly converted as a graph clustering problem,and then an objective function called the modularity function,which allows automatic selection of the number of clusters,is selected for graph clustering.The proposed algorithm does not make pairwise coreference decisions independently of each other.The experimental results on the Chinese ACE training corpus demonstrate that the proposed method is a feasible unsupervised algorithm for noun phrase coreference resolution.