Combining Labeled and Unlabeled Data for Text Classification with a Large Number of Categories
نویسنده
چکیده
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.
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Combining Labeled and Unlabeled Data for MultiClass Text Categorization
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