Semi-supervised Text Classification Using Partitioned EM

Text classification using a small labeled set and a large unlabeled data is seen as a promising technique to reduce the labor-intensive and time consuming effort of labeling training data in order to build accurate classifiers since unlabeled data is easy to get from the Web. In [16] it has been demonstrated that an unlabeled set improves classification accuracy significantly with only a small labeled training set. However, the Bayesian method used in [16] assumes that text documents are generated from a mixture model and there is a one-to-one correspondence between the mixture components and the classes. This may not be the case in many applications. In many real-life applications, a class may cover documents from many different topics, which violates the oneto-one correspondence assumption. In such cases, the resulting classifiers can be quite poor. In this paper, we propose a clustering based partitioning technique to solve the problem. This method first partitions the training docu...

• Type: Conference
• Year: 2004
• Where: DASFAA
• Authors: Gao Cong, Wee Sun Lee, Haoran Wu, Bing Liu
• Keywords: DASFAA 2004, Database, Large Unlabeled Data, Small Labeled Set, Unlabeled Data