A novel chinese text feature selection method based on probability latent semantic analysis

Authors:
Jiang Zhong;Xiongbing Deng;Jie Liu;Xue Li;Chuanwei Liang
Affiliations:
College of Computer Science, Chongqing University, Chongqing, China;College of Computer Science, Chongqing University, Chongqing, China;College of Computer Science, Chongqing University, Chongqing, China;School of Information Technology and Electrical Engineering, University of Queensland, QLD, Australia;Information Center of State Administration of Taxation, Laizhou, Shandong, China
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Year:
2010

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Abstract

Effective feature selection is essential to make the learning task efficient and more accurate In this paper, a novel Chinese text feature selection algorithm based on PLSA was presented for text classification, and it was compared with other effective feature selection methods on a benchmark of 8 text classification problem instances that were gathered from Sougou Lab's corpus The results show that this method could make SVM classifier have the best classification performance and more robust than other methods.