Abstract
Probabilistic topic models are broadly used to infer meaningful patterns of words over a mixture of latent topics that are commonly used for statistical analyses or as a proxy for supervised tasks. However, models such as Latent Dirichlet Allocation (LDA) assume independence between topic proportions due to the nature of the Dirichlet distribution; this effect is captured with other distributions such as the logistic normal distribution, resulting in a complex model. In this paper, we develop a probabilistic topic model using the generalized Dirichlet distribution (LGDA) in order to capture topic correlation while maintaining conjugacy. We make use of Expectation Propagation to approximate the posterior, resulting in a model that achieves more accurate inferences compared to variational inference. We evaluate the convergence of EP compared with the classical LDA by comparing the approximation to the marginal distribution. We show the obtained topics by LGDA and evaluate its predictive performance in two text classification tasks, outperforming the vanilla LDA.
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Notes
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We use an implementation of LDA where no smoothing is applied [6].
References
Bakhtiari, A.S., Bouguila, N.: A variational bayes model for count data learning and classification. Eng. Appl. Artif. Intell. 35, 176–186 (2014)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
Blei, D.M.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012). https://doi.org/10.1145/2133806.2133826
Blei, D.M., Kucukelbir, A., McAuliffe, J.D.: Variational inference: a review for statisticians. J. Am. Statist. Assoc. 112(518), 859–877 (2017)
Blei, D.M., Lafferty, J.D., et al.: A correlated topic model of science. Ann. Appl. Statist. 1(1), 17–35 (2007)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. In: Advances in Neural Information Processing Systems, pp. 601–608 (2002)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)
Bouguila, N.: Clustering of count data using generalized Dirichlet multinomial distributions. IEEE Trans. Knowl. Data Eng. 20(4), 462–474 (2008)
Boyd-Graber, J., Hu, Y., Mimno, D., et al.: Applications of topic models. Found. Trends® Inf. Retrieval 11(2–3), 143–296 (2017)
Caballero, K.L., Barajas, J., Akella, R.: The generalized Dirichlet distribution in enhanced topic detection. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 773–782. ACM (2012)
Connor, R.J., Mosimann, J.E.: Concepts of independence for proportions with a generalization of the Dirichlet distribution. J. Am. Statist. Association. 64(325), 194–206 (1969)
Dickey, J.M.: Multiple hypergeometric functions: probabilistic interpretations and statistical uses. J. Am. Statist. Assoc. 78(383), 628–637 (1983)
Gelman, A., Vehtari, A., Jylänki, P., Robert, C., Chopin, N., Cunningham, J.P.: Expectation propagation as a way of life. arXiv preprint arXiv:1412.4869 157 (2014)
Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Natl. Acad. Sci. 101(suppl 1), 5228–5235 (2004)
Hoffman, M.D., Blei, D.M., Wang, C., Paisley, J.: Stochastic variational inference. J. Mach. Learn. Res. 14(1), 1303–1347 (2013)
Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. 42(1–2), 177–196 (2001)
Ihou, K.E., Bouguila, N.: Variational-based latent generalized Dirichlet allocation model in the collapsed space and applications. Neurocomputing 332, 372–395 (2019)
Minka, T.: Estimating a Dirichlet distribution (2000)
Minka, T., Lafferty, J.: Expectation-propagation for the generative aspect model. In: Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, pp. 352–359. Morgan Kaufmann Publishers Inc. (2002)
Minka, T.P.: Expectation propagation for approximate Bayesian inference. In: Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, pp. 362–369. Morgan Kaufmann Publishers Inc. (2001)
Minka, T.P.: A family of algorithms for approximate Bayesian inference. Ph.D. thesis, Massachusetts Institute of Technology (2001)
Neal, R.M.: Probabilistic inference using Markov chain Monte Carlo methods (1993)
Srivastava, A., Sutton, C.: Autoencoding variational inference for topic models. arXiv preprint arXiv:1703.01488 (2017)
Steyvers, M., Griffiths, T.: Probabilistic topic models. Handb. Latent Semant. Anal. 427(7), 424–440 (2007)
Wong, T.T.: Generalized dirichlet distribution in Bayesian analysis. Appl. Math. Comput. 97(2–3), 165–181 (1998)
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Sumba, X., Bouguila, N. (2020). Improving Classification Using Topic Correlation and Expectation Propagation. In: Goutte, C., Zhu, X. (eds) Advances in Artificial Intelligence. Canadian AI 2020. Lecture Notes in Computer Science(), vol 12109. Springer, Cham. https://doi.org/10.1007/978-3-030-47358-7_51
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