Semi-Supervised Text Classification Using EM

By Kamal Nigam, Andrew McCallum, Tom Michael Mitchell. 2006

175 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Attention-based Graph Neural Network for Semi-supervised Learning (2018, methodology): “Traditionally, semi-supervised learning using both labeled and un-labled data has been solved using two different approaches - Graph Laplacian based algorithms solving for locally consistent solutions [47] and Expectation Maximization based algorithms [33] where true-labels of the unlabeled data poi”
Introduction to Information Retrieval (2008, unknown): “(2006) present an introduction to work in semi-supervised methods, including in particular chapters on using EM for semi-supervised text classification (Nigam et al. 2006) and on transductive SVMs (Joachims 2006b).”
Simple, robust, scalable semi-supervised learning via expectation regularization (2007, methodology): “Text classification has been a major target of semisupervised approaches, (Nigam et al., 2006), and we evaluate on the simulated/real auto/aviation (SRAA) task.”
A survey on semi-supervised learning (2019, background): “It has been particularly well studied in the context of naïve Bayes classifiers, which are inherently probabilistic (Nigam and Ghani 2000; Nigam et al. 2000, 2006).”
Text Classification Algorithms: A Survey (2019, methodology): “[211] developed a technique for text classification using expectation-maximization (EM) and generative models for semi-supervised learning with labeled and the unlabeled data in the field of text classifications.”
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data (2010, methodology): “9 Finally, one of the main targets for semi-supervised learning is text classification (Nigam et al., 2006), and we evaluate on the simulated/real auto/aviation (SRAA) task.”
Self-Training: A Survey (2022, unknown): “Data clustering uses a mixture model and assigns class labels to groups using the labeled data they include; and it constitutes the working principle of generative approaches [33, 25].”