A wide range of supervised learning algorithms has been applied to Text Categorization. However, the supervised learning approaches have some problems. One of them is that they require a large, often prohibitive, number of labeled training documents for accurate learning. Generally, acquiring class labels for training data is costly, while gathering a large quantity of unlabeled data is cheap. We here propose a new automatic text categorization method for learning from only unlabeled data using a bootstrapping framework and a feature projection technique. From results of our experiments, our method showed reasonably comparable performance compared with a supervised method. If our method is used in a text categorization task, building text categorization systems will become significantly faster and less expensive.
REF automatically collect training data using a large amount of unlabeled data and a small amount of seed information.
Learning With Unlabeled Data For Text Categorization Using A Bootstrapping And A Feature Projection Technique
Annual Meeting Of The Association For Computational Linguistics
Computer Science