Investigating unsupervised learning for text categorization bootstrapping
Alfio Gliozzo, Carlo Strapparava, Ido Dagan · 2005
We propose a generalized bootstrapping algorithm in which categories are described by relevant seed features. Our method introduces two unsupervised steps that improve the initial categorization step of the bootstrapping scheme: (i) using Latent Semantic space to obtain a generalized similarity measure between instances and features, and (ii) the Gaussian Mixture algorithm, to obtain uniform classification probabilities for unlabeled examples. The algorithm was evaluated on two Text Categorization tasks and obtained state-of-the-art performance using only the category names as initial seeds.