SVM-Based semantic text categorization for large scale web information organization

Authors:
Peng Fu;Deyun Zhang;Zhaofeng Ma;Hao Dong
Affiliations:
Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China;Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China;Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China;Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

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Abstract

Traditional web information service can't meet the demand of users getting personalized information timely and properly, which can be think as a kind of passive information organization method. In this paper, an adaptive and active information organization model in complex Internet environment is proposed to provide personalized information service and to automatically retrieve timely, relevant information. An SVM-based Semantic text categorization method is adopted to implement adaptive and active information retrieval. Performance experiment based on a prototype retrieval system manifests the proposed schema is efficient and effective.