Estimating a one-class naive Bayes text classifier

Nowadays more and more information extraction projects need to classify large amounts of text data. The common way to classify text is to build a supervised classifier trained on human-labeled positive and negative examples. In many cases, however, it is easy to label positive examples, but hard to label negative examples. In this paper, we address the problem of building a one-class classifier wh…