Semi-supervised Text Categorization by Considering Sufficiency and Diversity

In text categorization (TC), labeled data is often limited while unlabeled data is ample. This motivates semi-supervised learning for TC to improve the performance by exploring the knowledge in both labeled and unlabeled data. In this paper, we propose a novel bootstrapping approach to semi-supervised TC. First of all, we give two basic preferences, i.e., sufficiency and diversity for a possibly successful bootstrapping. After carefully considering the diversity preference, we modify the traditional bootstrapping algorithm by training the involved classifiers with random feature subspaces instead of the whole feature space. Moreover, we further improve the random feature subspace-based bootstrapping with some constraints on the subspace generation to better satisfy the diversity preference. Experimental evaluation shows the effectiveness of our modified bootstrapping approach in both topic and sentiment-based TC tasks.