Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28252
Title: A knowledge-based approach for unsupervised Chinese coreference resolution
Authors: Ngai, G 
Wang, CS
Keywords: Coreference Resolution
Modified K-means Clustering
Stacked Transformation-based Learning
Unsupervised Learning
Issue Date: 2007
Source: Computational linguistics and Chinese language processing, 2007, v. 12, no. 4, p. 459-484 How to cite?
Journal: Computational linguistics and Chinese language processing 
Abstract: Coreference resolution is the process of determining the entity that noun phrases refer to. A great deal of research has been done on this task in English, using approaches ranging from those based on linguistics to those based on machine learning. In Chinese, however, much less work has been done in this area. One reason for this is the lack of resources for Chinese natural language processing. This paper presents a knowledge-based, unsupervised clustering algorithm for Chinese coreference resolution that maximizes performance using freely and easily available resources. Experiments to demonstrate the efficacy of such an approach are performed on two data sets: TDT3 and ACE05, and the ACE value coreference resolution results achieved through our approach are 52.5% and 55.2% respectively. An oracle experiment using gold standard noun phrases achieved even more impressive results of 77.0% and 76.4%. To analyze the causes of errors, this paper also looks into false alarms and misses in documents.
URI: http://hdl.handle.net/10397/28252
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