Text Classification from Labeled and Unlabeled Documents Using

This paper shows that the accuracy of learned text classi ers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important because in many text classi cation problems obtaining training labels is expensive, while large quantities of unlabeled documents are readily available. We introduce an algorithm for learning from labeled and unlabeled documents based on the combination of Expectation-Maximization (EM) and a naive Bayes classi er. The algorithm rst trains a classi er using the available labeled documents, and probabilistically labels the unlabeled documents. It then trains a new classi er using the labels for all the documents, and iterates to convergence. This basic EM procedure works well when the data conform to the generative assumptions of the model. However these assumptions are often violated in practice, and poor performance can result. We present two extensions to the algorithm that improve classi cation accuracy under these conditions: (1) a weighting factor to modulate the contribution of the unlabeled data, and (2) the use of multiple mixture components per class. Experimental results, obtained using text from three di erent real-world tasks, show that the use of unlabeled data reduces classi cation error by up to 30%.