Abstract
Text classification is an important problem in Natural Language Processing. It differs from many other classification tasks by the large number of features that have to be used during training. One of the solution for reducing dimensionality of feature space, is the usage of Latent Dirichlet Allocation. After this step, the smaller problem can be solved using standard classifiers. In [11], authors propose combination of LDA and Softmax classifier called Multi-class sLDA, that does both tasks simultaneously. However, to use the method, we have to choose a number of topics - hyperparameter of the model. This step requires analysis and human supervision. In this paper, we propose Boost Multi-class sLDAmodel, based on ensemble of many Multi-class sLDA models, that does not require the choice of topic number. Moreover, our model achieves significantly better classification accuracy, than Multi-class sLDA for any number of topics.
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References
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B (Methodol.) 39(1), 1–38 (1977)
Freund, Y., Schapire, R.: A decision-theoretic generalization of online learning and an application to boosting. J. Comput. Syst. Sci. 55, 119–139 (1997)
Rubin, D.: Bayesianly justifiable and relevant frequency calculations for the applied statistician. Ann. Stat. 12(4), 1151–1172 (1984)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2001). https://doi.org/10.1007/978-0-387-84858-7
Blei, D., Ng, A., Jordan, M.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Griffiths, T., Steyvers, M.: Finding scientific topics. Proc. Natl. Acad. Sci. 101, 5228–5235 (2004). https://doi.org/10.1073/pnas.0307752101
Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, New York (2006)
Teh, Y.W., Jordan, M.I., Beal, M.J., Blei, D.M.: Hierarchical Dirichlet processes. J. Am. Stat. Assoc. 101(476), 1566–1581 (2006)
Mcauliffe, J.D., Blei, D.M.: Supervised topic models. In: Advances in Neural Information Processing Systems (2008)
Cao, J., Xia, T., Li, J., Zhang, Y., Tang, S.: A density-based method for adaptive LDA model selection. Neurocomputing 72(7–9), 1775–1781 (2008). 16th European Symposium on Artificial Neural Networks
Wang, C., Blei, D., Fei-Fei, L.: Simultaneous image classification and annotation. In: Computer Vision and Pattern Recognition (2009)
Arun, R., Suresh, V., Veni Madhavan, C.E., Narasimha Murthy, M.N.: On finding the natural number of topics with latent Dirichlet allocation: some observations. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010. LNCS (LNAI), vol. 6118, pp. 391–402. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13657-3_43
Mimno, D., Blei, D.: Bayesian checking for topic models. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (2011)
Almeida, T.A., Gomez Hidalgo, J.M., Yamakami, A.: Contributions to the study of SMS spam filtering: new collection and results. In: Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG 2011), Mountain View, CA, USA (2011)
Deveaud, R., Sanjuan, E., Bellot, P.: Accurate and effective latent concept modeling for ad hoc information retrieval. Revue des Sciences et Technologies de l’Information - Série Document Numérique, Lavoisier, 61–84 (2014)
Chang, J.: LDA: Collapsed Gibbs Sampling Methods for Topic Models. R package version 1.4.2 (2015). https://CRAN.R-project.org/package=lda
Blei, D., Kucukelbir, A., McAuliffe, J.: Variational inference: a review for statisticians. J. Am. Stat. Assoc. 112(518), 859–877 (2017)
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Jankowski, M. (2018). Boost Multi-class sLDA Model for Text Classification. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_59
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DOI: https://doi.org/10.1007/978-3-319-91253-0_59
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