TITLE

Example-based text categorization (EBTC): the key to automatic indexing and classification?

AUTHOR(S)
Xue Chunxiang; Hou Hanqing
PUB. DATE
September 2009
SOURCE
Indexer;Sep2009, Vol. 27 Issue 3, p117
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
The goal of text categorization is the automatic classification of documents into predefined categories. In this article Xue and Hou discuss the traditional, probability-theory-based method, using algorithms such as K-nearest neighbor (KNN), naïve Bayes, and support vector machine (SVM) and go on to describe the alternative example-based text categorization (EBTC) method, concluding that, although work to improve both the automatic construction of the example base and the classification algorithm must continue, EBTC has demonstrated its effectiveness for automatic indexing and classification and has decided advantages over other systems.
ACCESSION #
43603749

 

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