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Title:
a comparative study of tf*idf, lsi and multi-words for text classification
Author: Zhang Wen ; Yoshida Taketoshi ; Tang Xijin
Keyword: Data mining ; Indexing (of information) ; Information retrieval ; Natural language processing systems
Source: Expert Systems with Applications
Issued Date: 2011
Volume: 38, Issue:3, Pages:2758-2765
Indexed Type: ei
Department: (1) Laboratory for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China; (2) School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1 Ashahidai, Nomi, Ishikawa 923-1292, Japan; (3) Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
English Abstract: 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 TFIDF, 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. © 2010 Elsevier Ltd. All rights reserved.
Language: 英语
WOS ID: WOS:000284863200158
Citation statistics:
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/14095
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Zhang Wen,Yoshida Taketoshi,Tang Xijin. a comparative study of tf*idf, lsi and multi-words for text classification[J]. Expert Systems with Applications,2011-01-01,38(3):2758-2765.
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