Title :
An improved text feature selection method based on key words
Author :
Zhang, Hong-Wei ; Cao, Lian-Fang ; Feng, Su-Qin
Author_Institution :
Dept. of Electron., Xinzhou Teachers Univ., Xinzhou, China
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 :
feature extraction; information theory; pattern classification; support vector machines; text analysis; word processing; formal text representation; mutual information theory; support vector machine classifier; text feature selection method; text structural information theory; vector space model; Manganese; support vector machine; text classification; text feature selection; vector space model;
Conference_Titel :
Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on
Conference_Location :
Xiamen
Print_ISBN :
978-1-4244-6582-8
DOI :
10.1109/ICICISYS.2010.5658375
