A Clustering Approach for Unsupervised Chinese Coreference Resolution
Chi-Shing Wang, Grace Ngai · Meeting of the Association for Computational Linguistics · 2006
Coreference resolution is the process of identifying expressions that refer to the same entity. This paper presents a clustering algorithm for unsupervised Chinese coreference resolution. We investigate why Chinese coreference is hard and demonstrate that techniques used in coreference resolution for English can be extended to Chinese. The proposed system exploits clustering as it has advantages over traditional classification methods, such as the fact that no training data is required and it is easily extended to accommodate additional features. We conduct a set of experiments to investigate how noun phrase identification and feature selection can contribute to coreference resolution performance. Our system is evaluated on an annotated version of the TDT3 corpus using the MUC-7 scorer, and obtains comparable performance. We believe that this is the first attempt at an unsupervised approach to Chinese noun phrase coreference resolution.