The goal of this paper is to investigate additional principled unsupervised mechanisms within the initial classification step, applied to the text categorization.
(a) utilizing a Latent Semantic Space to obtain better similarity assessments between seeds and examples, and (b) applying a Gaussian Mixture (GM) algorithm, which provides a principled unsupervised estimation of classification probability.