Using EM to Classify Text from Labeledand Unlabeled DocumentsKamal
This paper shows that the accuracy of learned text classiiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is signiicant because in many important text classiication problems obtaining classiication labels is expensive, while large quantities of unlabeled documents are readily available. We present a theoretical argument showing that, under common assumptions, unlabeled data contain information about the target function. We then introduce an algorithm for learning from labeled and unlabeled text, based on the combination of Expectation-Maximization with a naive Bayes classiier. The algorithm rst trains a classiier using the available labeled documents, and probabilistically labels the unlabeled documents. It then trains a new classiier using the labels for all the documents, and iterates. Experimental results, obtained using text from three diierent real-world tasks, show that the use of unlabeled data reduces classiication error by up to 30%.