Learning to Separate Text Content and Style for Classification
Dell Zhang, Wee Sun Lee · 2006
Abstract. Many text documents naturally have two kinds of labels. For example, we may label web pages from universities according to their categories, such as “student ” or “faculty”, or according the source universities, such as “Cornell ” or “Texas”. We call one kind of labels the content and the other kind the style. Given a set of documents, each with both content and style labels, we seek to effectively learn to classify a set of documents in a new style with no content labels into its content classes. Assuming that every document is generated using words drawn from a mixture of two multinomial component models, one content model and one style model, we propose a method named Cartesian EM that constructs content models and style models through Expectation Maximization and performs classification of the unknown content classes transductively. Our experiments on real-world datasets show the proposed method to be effective for style independent text content classification. 1