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[Demo] Integration of text-and web-mining results in EpidVis

Samiha Fadloun 1 Arnaud Sallaberry 1 Alizé Mercier 2 Elena Arsevska 3 Pascal Poncelet 1 Mathieu Roche 4
1 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement
3 UMR ASTRE - Animal, Santé, Territoires, Risques et Ecosystèmes
4 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale
Abstract : The new and emerging infectious diseases are an incising threat to countries due to globalisation, movement of passengers and international trade. In order to discover articles of potential importance to infectious disease emergence it is important to mine the Web with an accurate vocabulary. In this paper, we present a new methodology that combines text-mining results and visualisation approach in order to discover associations between hosts and symptoms related to emerging infectious disease outbreaks.
Keywords : Epidemiology Query Visualization Text-mining
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01851520
Contributor : Samiha Fadloun <>
Submitted on : Monday, July 30, 2018 - 12:32:54 PM
Last modification on : Monday, October 19, 2020 - 10:55:06 AM
Long-term archiving on: : Wednesday, October 31, 2018 - 1:22:03 PM

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LIRMM | CNRS | IRSTEA | INRA | AGROPOLIS | ADVANSE | CIRAD | AGROPARISTECH | MIPS | UNIV-MONTPELLIER | INRAE

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Samiha Fadloun, Arnaud Sallaberry, Alizé Mercier, Elena Arsevska, Pascal Poncelet, et al.. [Demo] Integration of text-and web-mining results in EpidVis. NLDB: Natural Language Processing and Information Systems, Jun 2018, Paris, France. pp.437-440, ⟨10.1007/978-3-319-91947-8_45⟩. ⟨lirmm-01851520⟩

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