Semi-supervised Multinomial Naive Bayes for Text Classification by Leveraging Word-level Statistical Constraint - Zhao, Li; Huang, Minlie; Yao, Ziyu; Su, Rongwei; Jiang, Yingying; , Xiaoyan (Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016)
Multinomial Naive Bayes with Expectation Maximization (MNB-EM) is a standard semi-supervised learning method to augment Multinomial Naive Bayes (MNB) for text classification. Despite its success, MNB-EM is not stable, and may succeed or fail to improve MNB. We believe that this is because MNB-EM lacks the ability to preserve the class distribution on words. In this paper, we propose a novel method to augment MNB-EM by leveraging the word-level statistical constraint to preserve the class distribution on words. The word-level statistical constraints are further converted to constraints on document posteriors generated by MNB-EM. Experiments demonstrate that our method can consistently improve MNB-EM, and outperforms state-of-art baselines remarkably.