J. Boyd-graber, Y. Hu, and D. Mimno, Applications of topic models, Foundations and Trends R in Information Retrieval, vol.11, issue.2-3, pp.143-296, 2017.

D. Kim, F. Benjamin, . Swanson, C. Michael, E. B. Hughes et al., Refinery: An open source topic modeling web platform, Journal of Machine Learning Research, vol.18, issue.12, pp.1-5, 2017.

X. Wang, A. Mccallum, and X. Wei, Topical n-grams: Phrase and topic discovery, with an application to information retrieval, Seventh IEEE International Conference on Data Mining (ICDM), pp.697-702, 2007.

Q. Mei, X. Shen, and C. Zhai, Automatic labeling of multinomial topic models, Proceedings of the 13th ACM SIGKDD international conference on Knowledge Discovery and Data mining, pp.490-499, 2007.

K. Jey-han-lau, D. Grieser, T. Newman, and . Baldwin, Automatic labelling of topic models, Proceedings of Annual Meeting of ACL-HLT, vol.1, pp.1536-1545, 2011.

W. Kou, F. Li, and T. Baldwin, Automatic labelling of topic models using word vectors and letter trigram vectors, Asia Information Retrieval Symposium, pp.253-264, 2015.

M. Danilevsky, C. Wang, N. Desai, X. Ren, J. Guo et al., Automatic construction and ranking of topical keyphrases on collections of short documents, Proceedings of the 2014 SIAM International Conference on Data Mining, pp.398-406, 2014.

D. Magatti, S. Calegari, D. Ciucci, and F. Stella, Automatic labeling of topics, Intelligent Systems Design and Applications, 2009. ISDA'09. Ninth International Conference on, pp.1227-1232, 2009.

M. El-assady, R. Sevastjanova, F. Sperrle, D. Keim, and C. Collins, Progressive learning of topic modeling parameters: A visual analytics framework, IEEE Trans. on Visualization and Computer Graphics, 2017.

N. Aletras, T. Baldwin, J. H. Lau, and M. Stevenson, Evaluating topic representations for exploring document collections, Journal of the Association for Information Science and Technology, vol.68, issue.1, pp.154-167, 2017.

S. Katerina-t-frantzi, J. Ananiadou, and . Tsujii, The c-value/nc-value method of automatic recognition for multi-word terms, International Conference on Theory and Practice of Digital Libraries, pp.585-604, 1998.

M. David, . Blei, Y. Andrew, and M. Ng, Latent dirichlet allocation, Journal of Machine Learning Research (JMLR), vol.3, pp.993-1022, 2003.

D. M. Hanna-m-wallach, A. Mimno, and . Mccallum, Rethinking lda: Why priors matter, Advances in Neural Information Processing Systems, 1973.

J. Tang, S. Wu, J. Sun, and H. Su, Cross-domain collaboration recommendation, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.1285-1293, 2012.

N. Aletras and M. Stevenson, Evaluating topic coherence using distributional semantics, IWCS, vol.13, pp.13-22, 2013.

Z. Li, J. Li, Y. Liao, S. Wen, and J. Tang, Labeling clusters from both linguistic and statistical perspectives: A hybrid approach. Knowledge-Based Systems, vol.76, pp.219-227, 2015.

J. Ventura, C. Jonquet, M. Roche, and M. Teisseire, Biomedical term extraction: overview and a new methodology, Information Retrieval Journal, vol.19, issue.1, pp.59-99, 2016.
URL : https://hal.archives-ouvertes.fr/lirmm-01274539