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Investigating Unsupervised Learning for Text Categorization Bootstrapping
Alfio Gliozzo, Carlo Strapparava and Ido Dagan
In: HLT/EMNLP 2005, 6-8 Oct 2005, Vancouver, B.C., Canada.
AbstractWe 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-theart
performance using only the category
names as initial seeds. [Edit]
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