Paper: Scaling Semi-supervised Naive Bayes with Feature Marginals

ACL ID P13-1034
Title Scaling Semi-supervised Naive Bayes with Feature Marginals
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013

Semi-supervised learning (SSL) methods augment standard machine learning (ML) techniques to leverage unlabeled data. SSL techniques are often effective in text classification, where labeled data is scarce but large unlabeled corpora are readily available. However, existing SSL tech- niques typically require multiple passes over the entirety of the unlabeled data, meaning the techniques are not applicable to large corpora being produced today. In this paper, we show that improving marginal word frequency estimates using unlabeled data can enable semi-supervised text classification that scales to massive unlabeled data sets. We present a novel learning algorithm, which optimizes a Naive Bayes model to accord with statis- tics calculated from the unlabeled corpus. In experiments with text top...