# Tutorial on Probabilistic Topic Modeling: Additive Regularization for Stochastic Matrix Factorization

@inproceedings{Vorontsov2014TutorialOP, title={Tutorial on Probabilistic Topic Modeling: Additive Regularization for Stochastic Matrix Factorization}, author={Konstantin V. Vorontsov and Anna Potapenko}, booktitle={International Joint Conference on the Analysis of Images, Social Networks and Texts}, year={2014} }

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.

## 60 Citations

### Additive Regularization of Topic Models for Topic Selection and Sparse Factorization

- Computer ScienceSLDS
- 2015

A simple entropy regularization for topic selection in terms of Additive Regularization of Topic Models (ARTM) is proposed, a multicriteria approach for combining regularizers.

### Non-Bayesian Additive Regularization for Multimodal Topic Modeling of Large Collections

- Computer ScienceTM@CIKM
- 2015

The ability of non-Bayesian regularization to combine modalities, languages and multiple criteria to find sparse, diverse, and interpretable topics is demonstrated.

### STABILITY OF TOPIC MODELING VIA MODALITY REGULARIZATION

- Computer Science
- 2020

This paper considers the use of additional information in the context of the stability problem of topic modeling, and shows that using side information as an additional modality improves topics stability without significant quality loss of the model.

### Fast and modular regularized topic modelling

- Computer Science2017 21st Conference of Open Innovations Association (FRUCT)
- 2017

A non-Bayesian multiobjective approach called the Additive Regularization of Topic Models (ARTM) is developed, based on regularized Maximum Likelihood Estimation (MLE), and it is shown that many of the well-known Bayesian topic models can be re-formulated in a much simpler way using the regularization point of view.

### Analyzing the Influence of Hyper-parameters and Regularizers of Topic Modeling in Terms of Renyi Entropy

- Computer ScienceEntropy
- 2020

This paper proposes a novel approach for analyzing the influence of different regularization types on results of topic modeling and concludes that regularization may introduce unpredictable distortions into topic models that need further research.

### Convergence of the Algorithm of Additive Regularization of Topic Models

- Computer Science, MathematicsProceedings of the Steklov Institute of Mathematics
- 2021

A modification of the algorithm is proposed that improves the convergence without additional time and memory costs and both accelerates the convergence and improves the value of the criterion to be optimized.

### Additive Regularization for Topic Modeling in Sociological Studies of User-Generated Texts

- Computer ScienceMICAI
- 2016

It is shown with human evaluations that ARTM is better for mining topics on specific subjects, finding more relevant topics of higher or comparable quality than developing LDA extensions.

### BigARTM: Open Source Library for Regularized Multimodal Topic Modeling of Large Collections

- Computer ScienceAIST
- 2015

The BigARTM open source project is announced for regularized multimodal topic modeling of large collections and several experiments on Wikipedia corpus show that BigartM performs faster and gives better perplexity comparing to other popular packages, such as Vowpal Wabbit and Gensim.

### Using Topic Modeling to Improve the Quality of Age-Based Text Classification

- Computer Science
- 2021

This paper formulated this problem as a binary classification task and developed a topic-informed machine learning classifier for resolving this problem, and compared three common topic modeling techniques to obtain document topic distribution vectors.

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It is shown that a robust topic model, which distinguishes specific, background and topic terms, doesn't need Dirichlet regularization and provides controllably sparse solution.

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