Abstract
A major difficulty of supervised approaches for text classification is that they require a great number of training instances in order to construct an accurate classifier. This paper proposes a semi-supervised method that is specially suited to work with very few training examples. It considers the automatic extraction of unlabeled examples from the Web as well as an iterative integration of unlabeled examples into the training process. Preliminary results indicate that our proposal can significantly improve the classification accuracy in scenarios where there are less than ten training examples available per class.
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