An extension of the Aspect PLSA Model to Active and Semi-supervised Learning for Text Classification
In this paper, we address the problem of learning aspect models with partially labeled examples. We propose a method which benefits from both semi-supervised and active learning frameworks. In particular, we combine a semi-supervised extension of the PLSA algorithm with two active learning techniques. We perform experiments over four different datasets and show the effectiveness of the combination of the two frameworks.