Web Text Categorization Based on Latent Semantic Analysis

摘要：

Traditional text categorization methods are difficult to deal with the high dimensionality characteristic of the text document based on semantic concept of the words.This paper proposed a method to Web text categorization based on latent semantic analysis. Textual data was mapped into a lower space. The proposed approach used the singular-value decomposition to derive a latent semantic space.The SVM is used to text categorization in the semantic space.Experimental results show that the method is effective on the performance of the text categorization.

关键词： latent semantic analysis Web text categorization support vector machine

作者: WANG Jianfeng YUAN Jinsha

作者单位: Department of Electronic and Communication Engineering North China Electric Power University Baoding,Hebei province, 071003, China

会议类型: 国际会议

会议名称: 第一届国际计算机新科技与教育学术会议(Proceedings of the First International Conference on Computer Science & Education ICCSE2006)

会议地点: 厦门

会议语种:英文

页码: 826-828

在线出版日期: 2006-07-27（万方平台首次上网日期，不代表论文的发表时间）