关键词: 文本分类, 特征降维, 特征选择, 概念映射, 《知网》

Abstract: Reducing the high dimension of feature vectors is an important issue in text classification. After studying current technique of feature reduction, a new method based on concept mapping is proposed. A subset of features is selected by traditional method of feature selection. Every feature in subset is mapped into the semantic dictionary and then selected again. The approach can not only get rid of redundant features but also preserve the semantic information of text. The results of experiments show that this method has improved effectively the precision of the text classification.

Key words: text classification, feature reduction, feature selection, concept mapping, HowNet