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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.
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https://hal.archives-ouvertes.fr/hal-01236585
Contributor : Massih-Reza Amini <>
Submitted on : Tuesday, December 1, 2015 - 9:29:06 PM
Last modification on : Monday, April 20, 2020 - 11:24:02 AM

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Mikhail Kuznetsov, Marianne Clausel, Massih-Reza Amini, Eric Gaussier, Vadim Strijov. Supervised topic classification for modeling a hierarchical conference structure. 22nd International Conference on Neural Information Processing (ICONIP 2015), Nov 2015, Istanbul, Turkey. pp.90-97, ⟨10.1007/978-3-319-26532-2_11⟩. ⟨hal-01236585⟩

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