关键词: 概念语义空间, 文本特征选择, 非负矩阵分解

Abstract: Text feature selection is a key technology of text classification and information extraction.For text classification with high dimensional sparse feature vector problem，a feature extraction method based on non-negative matrix factorization and concept semantic space is presented.This method gives the interpretation of the theme and better reflects the local characteristics of the text by adding the non-negative limitation to the matrix factorization.Experimental results show higher accuracy of the classification is achieved in local semantic space.

Key words: concept semantic space, text feature selection, non-negative matrix factorization