Large Scale Text Classification using Semisupervised Multinomial Naive Bayes

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Dropout Training as Adaptive Regularization (2013, background): “Methods Labeled +Unlabeled MNB-Uni [19] 83.”
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks (2015, methodology): “In the setting of semisupervised learning, we also compare with classical semisupervised approaches, Naive Bayes with EM (NB+EM) [25] and label propagation (LP) [31].”
Large Scale Sentiment Learning with Limited Labels (2017, background): “Su et al [19] propose a semi-supervised extension of MNB, SFE, that uses the estimates of word probabilities obtained from unlabeled data and class conditional word probabilities learned from the labeled data, to learn the MNB parameters.”
Linguistic Structured Sparsity in Text Categorization (2014, methodology): “This contrasts with typical semi-supervised learning methods for text categorization that combine unlabeled and labeled data within a generative model, such as multinomial naı̈ve Bayes, via expectation-maximization (Nigam et al., 2000) or semi-supervised frequency estimation (Su et al., 2011).”