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Vorontsov, Konstantin, And Anna Potapenko. 2014. "Tutorial On Probabilistic Topic Modeling: Additive Regularization For Stochast.pdf

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Preview of Vorontsov, Konstantin, and Anna Potapenko. 2014. "Tutorial on Probabilistic Topic Modeling: Additive Regularization for Stochast
🔗 Source: machinelearning.ru
📊 Size: 460 KB
📄 Pages: 18 pages
⬇️ Downloads: 833

Summary

Konstantin Vorontsov and Anna Potapenko present a tutorial on probabilistic topic modeling, introducing a novel non-Bayesian approach called Additive Regularization of Topic Models (ARTM). ARTM simplifies theory and reduces barriers to entry into topic modeling research by removing redundant probabilistic assumptions and providing simple inference for combined and multi-objective topic models. The approach is based on stochastic matrix factorization and additive regularization, allowing for the combination of regularizers that improve multiple criteria at once without significant loss of likelihood. The tutorial covers popular topic models, including Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA), and demonstrates the instability of these models, motivating the need for problem-oriented additive regularization.

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Konstantin Vorontsov and Anna Potapenko present a tutorial on probabilistic topic modeling, introducing a novel non-Bayesian approach called Additive...

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  • File Format: PDF
  • File Size: 460 KB
  • Pages: 18
  • Language: EN
  • Total Downloads: 833
  • Last Updated: 3 weeks ago

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