DocumentCode
2691347
Title
The Improvment of Text Feature Selection Method Based on Key Words
Author
Jian-Fang, Cao ; Hong-Bin, Wang
Author_Institution
Dept. of Comput. Sci., Xinzhou Teachers Univ., Xinzhou, China
fYear
2012
fDate
7-9 July 2012
Firstpage
140
Lastpage
143
Abstract
Vector space model is commonly used in the formal representation on text, but this approach would not highlight the features which play a key role in the text contents. An improved feature selection method based on key words was proposed, which uses text structural information and mutual information theory to extract key words on text content. Through using support vector machine (SVM) classifier to test, results showed that classification accuracy has improved significantly.
Keywords
learning (artificial intelligence); support vector machines; text analysis; vectors; SVM classifier; formal representation; improved feature selection method; key words; mutual information theory; support vector machine classifier; text feature selection method; text structural information; vector space model; support vector machine; text classification; text feature selection; vector space model;
fLanguage
English
Publisher
ieee
Conference_Titel
Computing, Measurement, Control and Sensor Network (CMCSN), 2012 International Conference on
Conference_Location
Taiyuan
Print_ISBN
978-1-4673-2033-7
Type
conf
DOI
10.1109/CMCSN.2012.36
Filename
6245832
Link To Document