Semi-Supervised Multinomial Naive Bayes for Text Classification by Leveraging Word-Level Statistical Constraint

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.

Authors

• Li Zhao Shaanxi Normal University
• Minlie Huang Tsinghua University, Tsinghua University The CoAI Group, DCST, Tsinghua University
• Ziyu Yao George Mason University
• Rongwei Su
• Yingying Jiang
• Xiaoyan Zhu Xi'an Jiaotong University

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