Combining labeled and unlabeled data for text classification with a large number of categories

We develop a framework to incorporate unlabeled data in the error-correcting output coding (ECOC) setup by decomposing multiclass problems into multiple binary problems and then use co-training to learn the individual binary classification problems. We show that our method is especially useful for classification tasks involving a large number of categories where co-training doesn't perform very well by itself and when combined with ECOC, outperforms several other algorithms that combine labeled and unlabeled data for text classification in terms of accuracy, precision-recall tradeoff, and efficiency.