Аннотация:
Probabilistic topic modeling is a powerful tool for statistical text analysis, which has been recently developing mainly within the framework of graphical models and Bayesian inference. We propose an alternative approach - Additive Regularization of Topic Models (ARTM). Our framework is free of redundant probabilistic assumptions and dramatically simplifies the inference of multi-objective topic models. Also we hold a non-probabilistic view of the EM-algorithm as a simple iteration method for solving a system of equations for a stationary point of the optimization problem.
