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Author and Title
Kamal Nigam, Andrew McCallum, and Tom M. Mitchell. Semi-Supervised Text Classification Using EM.

Abstract
For several decades, statisticians have advocated using a combination of labeled and unlabeled data to train classifiers by estimating parameters of a generative model through iterative Expectation-Maximization (EM) techniques. This chapter explores the effectiveness of this approach when applied to the domain of text classification. Text documents are represented here with a bag-of-words model, which leads to a generative classification model based on a mixture of multinomials. This
model is an extremely simplistic representation of the complexities of written text. This chapter explains and illustrates three key points about semi-supervised learning for text classification with generative models. First, despite the simplistic representation, some text domains have a high positive correlation between generative model probability and classification accuracy. In these domains, a straightforward application of EM with the naive Bayes text model works well. Second, some text
domains do not have this correlation. Here we can adopt a more expressive and appropriate generative model that does have a positive correlation. In these domains, semi-supervised learning again improves classification accuracy. Finally, EM suffers from the problem of local maxima, especially in high dimension domains such as text classification. We demonstrate that deterministic annealing, a variant of EM, can help overcome the problem of local maxima and increase classification accuracy further when the generative model is appropriate.

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