期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 38, 期 3, 页码 2758-2765出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2010.08.066
关键词
Text representation; TF*IDF; LSI; Multi-word; Text classification; Information retrieval; Text categorization
类别
资金
- National Natural Science Foundation of China [90718042, 60873072, 60803023]
- National Hi-Tech RD Plan of China [2007AA010303, 2007AA01Z179]
- National Basic Research Program [2007CB310802]
- Foundation of Young Doctors of Institute of Software, Chinese Academy of Sciences [ISCAS2009-DR03]
One of the main themes in text mining is text representation, which is fundamental and indispensable for text-based intellegent information processing. Generally, text representation inludes two tasks: indexing and weighting. This paper has comparatively studied TF*IDF, LSI and multi-word for text representation. We used a Chinese and an English document collection to respectively evaluate the three methods in information retreival and text categorization. Experimental results have demonstrated that in text categorization, LSI has better performance than other methods in both document collections. Also, LSI has produced the best performance in retrieving English documents. This outcome has shown that LSI has both favorable semantic and statistical quality and is different with the claim that LSI can not produce discriminative power for indexing. (C) 2010 Elsevier Ltd. All rights reserved.
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