Combining Labeled and Unlabeled Data for MultiClass Text Categorization

Supervised learning techniques for text classi cation often require a large number of labeled examples to learn accurately. One way to reduce the amountoflabeled datarequired is to develop algorithms that can learn e ectively from a small number of labeled examples augmented with a large number of unlabeled examples. Current text learning techniques for combining labeled and unlabeled, such as EM and Co-Training, are mostly applicable for classi cation tasks with a small number of classes and do not scale up well for large multiclass problems. In this paper, we develop a framework to incorporate unlabeled data in the Error-Correcting Output Coding (ECOC) setup by rst decomposing multiclass problems into multiple binary problemsand then using Co-Trainingto learn the individual binary classi cation problems. We show that our method is especially useful for text classi cation tasks involving a large number of categories and outperforms other semi-supervised learning techniques such as EM...

• Type: Conference
• Year: 2002
• Where: ICML
• Authors: Rayid Ghani
• Keywords: Classi Cation Problems, Classi Cation Tasks, ICML 2002, Machine Learning, Text Classi Cation