Multi-level text classification method based on latent semantic analysis

被引：0
|
作者：
Shi, Hongxia [1 ]
Wei, Guiyi [1 ]
Pan, Yun
机构：
[1] Zhejiang Gongshang Univ, Coll Comp & Informat Engn, Hangzhou, Peoples R China
来源：
ICEIS 2007: PROCEEDINGS OF THE NINTH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS: SOFTWARE AGENTS AND INTERNET COMPUTING | 2007年
关键词：
text classification; LSA; vector space model;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In multi-level text classification, all categories have level relation. The categories in the same layer have a certain generality. By applying LSA theory to multi-level text classification, the words' semantic relationship is better represented and their weight equations are adjusted so they are more reasonable. This method extends the traditional vector space model to LSA space model and consequent experiments got very good results.
引用
收藏
页码：320 / +
页数：2
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