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International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 1
A Hybrid Classifier Approach for Web Retrieved Documents Classification
Las Vegas, Nevada
April 05-April 07
ISBN: 0-7695-2108-8
Razvan Stefan Bot, New Jersey Institute of Technology
Yi-fang Brook Wu, New Jersey Institute of Technology
Xin Chen, New Jersey Institute of Technology
Quanzhi Li, New Jersey Institute of Technology
The paper presents a hybrid technique for the classification of web returned hits into concept hierarchies. The technique involves a combination of manual and automatic classifiers. At first, all web returned documents are assigned to human defined categories using manual classifiers, and then automatic classifiers are used to generate a concept hierarchy for each of these categories. The results of the evaluation reveal the following: (a) for polysemous queries, our system is able to generate meaningful categories corresponding to (but not limited to), the different semantic facets of the queries; (b) as expected, for non-polysemous queries the system generates fewer categories; (c) the hierarchy precision of the concept hierarchies generated for polysemous queries is found to be significantly better when compared to the one obtained using a baseline system.
Index Terms:
information retrieval, automatic classification, manual classification, hybrid classification, concept hierarchy
Citation:
Razvan Stefan Bot, Yi-fang Brook Wu, Xin Chen, Quanzhi Li, "A Hybrid Classifier Approach for Web Retrieved Documents Classification," itcc, vol. 1, pp.326, International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 1, 2004
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