%0 Conference Proceedings
%T Readitopics: Make Your Topic Models Readable via Labeling and Browsing
%+ Entrepôts, Représentation et Ingénierie des Connaissances (ERIC)
%+ Laboratoire Hubert Curien (LHC)
%+ Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS)
%+ ADVanced Analytics for data SciencE (ADVANSE)
%A Velcin, Julien
%A Gourru, Antoine
%A Giry-Fouquet, Erwan
%A Gravier, Christophe
%A Roche, Mathieu
%A Poncelet, Pascal
%< avec comité de lecture
%( 27th International Joint Conference on Artificial Intelligence
%B IJCAI: International Joint Conference on Artificial Intelligence
%C Stockholm, Sweden
%8 2018-07-13
%D 2018
%Z Computer Science [cs]/Databases [cs.DB]Conference papers
%X Readitopics provides a new tool for browsing a textual corpus that showcases several recent work for labeling topic models and estimating topic coherence. We will demonstrate the potential of these techniques to get a deeper understanding of the topics that structure different kinds of datasets. This tool is provided as a Web demo but it can be easily installed to experiment with your own dataset. It can be further extended to deal with more advanced topic modeling techniques.
%G English
%2 https://hal-lirmm.ccsd.cnrs.fr/lirmm-01910611/document
%2 https://hal-lirmm.ccsd.cnrs.fr/lirmm-01910611/file/readitopics2018.pdf
%L lirmm-01910611
%U https://hal-lirmm.ccsd.cnrs.fr/lirmm-01910611
%~ UNIV-ST-ETIENNE
%~ CIRAD
%~ IOGS
%~ AGROPARISTECH
%~ CNRS
%~ UNIV-LYON1
%~ UNIV-LYON2
%~ IRSTEA
%~ PARISTECH
%~ ERIC
%~ ADVANSE
%~ LIRMM
%~ AGROPOLIS
%~ TETIS
%~ AGREENIUM
%~ MIPS
%~ UNIV-MONTPELLIER
%~ LYON2
%~ UDL
%~ UNIV-LYON
%~ INRAE
%~ INRAEOCCITANIEMONTPELLIER
%~ UM-2015-2021
%~ TEST3-HALCNRS
%~ TEST4-HALCNRS
%~ MATHNUM
%~ TEST5-HALCNRS