Additive regularization of topic models
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| 008 | 210128e20151001xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10994-014-5476-6 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-014-5476-6 | ||
| 245 | 0 | 0 | |a Additive regularization of topic models |h [Elektronische Daten] |c [Konstantin Vorontsov, Anna Potapenko] |
| 520 | 3 | |a Probabilistic topic modeling of text collections has been recently developed mainly within the framework of graphical models and Bayesian inference. In this paper we introduce an alternative semi-probabilistic approach, which we call additive regularization of topic models (ARTM). Instead of building a purely probabilistic generative model of text we regularize an ill-posed problem of stochastic matrix factorization by maximizing a weighted sum of the log-likelihood and additional criteria. This approach enables us to combine probabilistic assumptions with linguistic and problem-specific requirements in a single multi-objective topic model. In the theoretical part of the work we derive the regularized EM-algorithm and provide a pool of regularizers, which can be applied together in any combination. We show that many models previously developed within Bayesian framework can be inferred easier within ARTM and in some cases generalized. In the experimental part we show that a combination of sparsing, smoothing, and decorrelation improves several quality measures at once with almost no loss of the likelihood. | |
| 540 | |a The Author(s), 2014 | ||
| 690 | 7 | |a Probabilistic topic modeling |2 nationallicence | |
| 690 | 7 | |a Regularization of ill-posed problems |2 nationallicence | |
| 690 | 7 | |a Probabilistic latent sematic analysis |2 nationallicence | |
| 690 | 7 | |a Latent Dirichlet allocation |2 nationallicence | |
| 690 | 7 | |a EM-algorithm |2 nationallicence | |
| 700 | 1 | |a Vorontsov |D Konstantin |u Department of Intelligent Systems at Dorodnicyn Computing Centre of RAS, Institute of Physics and Technology, Moscow, Russia |4 aut | |
| 700 | 1 | |a Potapenko |D Anna |u Computer Science Department, The Higher School of Economics, Moscow, Russia |4 aut | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 101/1-3(2015-10-01), 303-323 |x 0885-6125 |q 101:1-3<303 |1 2015 |2 101 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-014-5476-6 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10994-014-5476-6 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Vorontsov |D Konstantin |u Department of Intelligent Systems at Dorodnicyn Computing Centre of RAS, Institute of Physics and Technology, Moscow, Russia |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Potapenko |D Anna |u Computer Science Department, The Higher School of Economics, Moscow, Russia |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Machine Learning |d Springer US; http://www.springer-ny.com |g 101/1-3(2015-10-01), 303-323 |x 0885-6125 |q 101:1-3<303 |1 2015 |2 101 |o 10994 | ||