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.
2010, Vol. 30 
