Document Clustering Description Extraction and Its Application

Authors:
Chengzhi Zhang;Huilin Wang;Yao Liu;Hongjiao Xu
Affiliations:
Department of Information Management, Nanjing University of Science & Technology, Nanjing, China 210093 and Institute of Scientific & Technical Information of China, Beijing, China 100038;Institute of Scientific & Technical Information of China, Beijing, China 100038;Institute of Scientific & Technical Information of China, Beijing, China 100038;Institute of Scientific & Technical Information of China, Beijing, China 100038
Venue:
ICCPOL '09 Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy
Year:
2009

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Abstract

Document clustering description is a problem of labeling the clustering results of document collection clustering. It can help users determine whether one of the clusters is relevant to their information requirements or not. To resolve the problem of the weak readability of document clustering results, a method of automatic labeling document clusters based on machine learning is put forward. Clustering description extraction in application to topic digital library construction is introduced firstly. Then, the descriptive results of five models are analyzed respectively, and their performances are compared.