Probabilistic topic modeling of text collections is a powerful tool for statistical text analysis. In this tutorial we introduce a novel non-Bayesian approach, called Additive Regularization of Topic Models. ARTM is free of redundant probabilistic assumptions and provides a simple inference for many combined and multi-objective topic models.
CITATION STYLE
Vorontsov, K., & Potapenko, A. (2014). Tutorial on probabilistic topic modeling: Additive regularization for stochastic matrix factorization. Communications in Computer and Information Science, 436, 29–46. https://doi.org/10.1007/978-3-319-12580-0_3
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