关键词: 属性选择, 半监督学习, 短文本, 文本分类, 集成学习

Abstract: In order to solve the data scarcity problem of massive short text categorization, a semi-supervised short text categorization method based on attribute selection was presented. An attribute selection algorithm based on ReliefF and independence measures was used to overcome the limitation of the attributes independence assumption by deleting irrelevant or redundant attributes, and an ensemble algorithm based on Expectaion-Maximization (EM) was used to resolve the problems of sensitivity to initial values in semi-supervised EM algorithm. The experiments on real corpus show that the proposed method can more effectively and stably utilize the unlabeled examples to improve classification generalization.

Key words: attribute selection, semi-supervised learning, short text, text categorization, ensemble learning