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A Fuzzy-Based Approach for Text Representation in Text Categorization

Authors: Son Doan;

A Fuzzy-Based Approach for Text Representation in Text Categorization

Abstract

Document representation is one of the most important tasks in text processing, especially in text categorization. This task has many applications that include document management, information retrieval, text routing, etc. In this paper, the author proposes a novel scheme for text representation based on fuzzy set theory. A new algorithm for choosing a term set that characterizes a document in the corpus is given under the view of fuzzy set. Experimental results applied to text categorization problem using the relevance feedback technique show that our proposed method reduced the number of dimensions and achieves higher performances compared to other baseline methods. In addition, it also produces results that compare favorably to the result achieved with the all vocabulary method

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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