In this paper we investigate the problem of supervised latent modeling 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 expertgiven 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.
CITATION STYLE
Kuznetsov, M., Clausel, M., Amini, M. R., Gaussier, E., & Strijov, V. (2015). Supervised topic classification for modeling a hierarchical conference structure. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9489, pp. 90–97). Springer Verlag. https://doi.org/10.1007/978-3-319-26532-2_11
Mendeley helps you to discover research relevant for your work.