Term Graph Model for Text Classification
Lecture notes in computer sciencePublished 1 January 2005
Wei Wang, Diep Nguyen Thi Bich, Xuemin Lin
Citations49
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
This work proposes a term graph model to represent not only the content of a document but also the relationship among the keywords, and demonstrates that the new model enables to define new similarity functions, such as considering rank correlation based on PageRank-style algorithms, for the classification purpose.
Abstract
Most existing text classification methods (and text mining methods at large) are based on representing the documents using the traditional vector space model. We argue that important information, such as the relationship among words, is lost. We propose a term graph model to represent not only the content of a document but also the relationship among the keywords. We demonstrate that the new model enables us to define new similarity functions, such as considering rank correlation based on PageRank-style algorithms, for the classification purpose. Our preliminary results show promising results of our new model.
Keywords
Computer Science
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