Supervised topic classification for modeling a hierarchical conference structure

Abstract : In this paper we investigate the problem of supervised latent modelling for extracting topic hierarchies from data. The supervised part is given in the form of expert information over document-topic correspondence. To exploit the expert information we use a regularization term that penalizes the difference between a predicted and an expert-given model. We hence add the regularization term to the log-likelihood function and use a stochastic EM based algorithm for parameter estimation. The proposed method is used to construct a topic hierarchy over the proceedings of the European Conference on Operational Research and helps to automatize the abstract submission system.
Type de document :
Communication dans un congrès
Sabri Arik; Tingwen Huang; Weng Kin Lai; Qingshan Liu. 22nd International Conference on Neural Information Processing (ICONIP 2015), Nov 2015, Istanbul, Turkey. Springer, 9489 (Part I), pp.90-97, 2015, Lecture Notes in Computer Science. 〈http://www.iconip2015.org/〉. 〈10.1007/978-3-319-26532-2_11〉
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https://hal.archives-ouvertes.fr/hal-01236585
Contributeur : Massih-Reza Amini <>
Soumis le : mardi 1 décembre 2015 - 21:29:06
Dernière modification le : vendredi 24 novembre 2017 - 13:26:25

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Mikhail Kuznetsov, Marianne Clausel, Massih-Reza Amini, Eric Gaussier, Vadim Strijov. Supervised topic classification for modeling a hierarchical conference structure. Sabri Arik; Tingwen Huang; Weng Kin Lai; Qingshan Liu. 22nd International Conference on Neural Information Processing (ICONIP 2015), Nov 2015, Istanbul, Turkey. Springer, 9489 (Part I), pp.90-97, 2015, Lecture Notes in Computer Science. 〈http://www.iconip2015.org/〉. 〈10.1007/978-3-319-26532-2_11〉. 〈hal-01236585〉

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