A practical hypertext catergorization method using links and incrementally available class information

Abstract

As WWW grows at an increasing speed, a classifier targeted at hypertext has become in high demand. While document categorization is quite a mature, the issue of utilizing hypertext structure and hyperlinks has been relatively unexplored. In this paper, we propose a practical method for enhancing both the speed and the quality of hypertext categorization using hyperlinks. In comparison against a recently proposed technique that appears to be the only one of the kind, we obtained up to 18.5% of improvement in effectiveness while reducing the processing time dramatically. We attempt to explain through experiments what factors contribute to the improvement.

Costco: Robust Content and Structure Constrained Clustering of Networked Documents

“…[4] uses factorized model to combine the content model and the link model. [14] tackles the problem by using the relaxation labeling technique. Besides the vast amount of work on link-enhanced text classification, there are increasing number of work focusing on link-enhanced clustering.…”

“…Exploiting link information of networked documents to enhance text classification has been studied extensively in the research community [3,4,6,14]. It is found that, although both content attributes and links can independently form reasonable text classifiers, an algorithm that exploits both information sources has the potential to improve the classification [2,10].…”

Text and Hypertext Categorization

“…TC is used in many application contexts, ranging from automatic document indexing based on a controlled vocabulary (Borko and Bernick 1963;Gray and Harley 1971;Field 1975), to document filtering (Amati and Crestani 1999;Iyer, Lewis et al 2000;Kim, Hahn et al 2000), word sense disambiguation (Gale, Church et al 1992;Escudero, Marquez et al 2000), population of hierarchical catalogues of Web resources (Chakrabarti, Dom et al 1998;Attardi, Gulli et al 1999;Oh, Myaeng et al 2000), and in general any application requiring document organization or selective and adaptive document dispatching.…”

“…It was concluded that the intelligent use of this information helps improve the accuracy of the different classifiers used. (Oh, Myaeng et al 2000) reported some observations on a collection of online Korean encyclopaedia articles. They used system-predicted categories of the linked neighbours of a test document to reinforce the classification decision on that document and they obtained a 13% improvement over the baseline performance when using local text alone.…”

Neighbourhood Exploitation in Hypertext Categorization

“…Oh et al [6] reported some observations on a collection of online Korean encyclopaedia articles. They used system-predicted categories of the linked neighbours of a test document to reinforce the classification decision on that document and they obtained a 13% improvement over the baseline performance when using local text alone.…”