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JACIII Vol.20 No.5 pp. 755-764
doi: 10.20965/jaciii.2016.p0755
(2016)

Paper:

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Chinese Person Name Disambiguation Based on Two-Stage Clustering

Jie Zhou*, Bicheng Li**, and Yongwang Tang*

*Zhengzhou Information Science and Technology Institute
Zhengzhou 450002, China

**Computer Science and Technology Institute, Huaqiao University
Xiamen 361021, China

Received:
May 5, 2016
Accepted:
June 13, 2016
Published:
September 20, 2016
Creative Commons License
Keywords:
person name disambiguation, two-stage clustering, core evidence, social relation, important feature selection
Abstract
Person name clustering disambiguation is the process that partitions name mentions according to corresponding target person entities in reality. The existed methods can not realize effective identification of important features to disambiguate person names. This paper presents a method of Chinese person name disambiguation based on two-stage clustering. This method adopts a stage-by-stage processing model to identify and utilize different types of important features. Firstly, we extract three kinds of core evidences namely direct social relation, indirect social relation and common description prefix, recognize document-pairs referring to the same person entity, and realize initial clustering of person names with high precision. Then, we take the result of initial clustering as new initial input, utilize the statistical properties of multi-documents to recognize and evaluate important features, and build a double-vector representation of clusters (cluster feature vector and important feature vector). Based on the processes above, the final clustering of person names is generated, and the recall of clustering is improved effectively. The experiments have been conducted on the dataset of CLP2010 Chinese person names disambiguation, and experimental results show that this method has good performance in person name clustering disambiguation.
Cite this article as:
J. Zhou, B. Li, and Y. Tang, “Chinese Person Name Disambiguation Based on Two-Stage Clustering,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.5, pp. 755-764, 2016.
Data files:
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