Knowledge Discovery from Texts on Agriculture Domain

Mathieu Roche 1, 2
1 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Large amounts of textual data related to the agriculture domain are now available. Knowledge discovery becomes a crucial issue for research organizations, decision makers, and users. Our work investigates the use of \emph{Text Mining} methodologies in order to tackle several issues such as Animal Disease Surveillance, Open Data in Agriculture Domain, Information Extraction from Experimental Data. In this context, we have defined a new Knowledge Discovery from Texts (KDT) process applied to the agriculture domain (http://textmining.biz/agroNLP.html). This one is divided into four steps: (i) data acquisition, (ii) information retrieval, (iii) information extraction and disambiguation, (iv) visualization and evaluation. In this KDT process applied to specific use-cases, the integration of expert knowledge has a key role.
Type de document :
Communication dans un congrès
MISC: Modelling and Implementation of Complex Systems, May 2016, Constantine, Algeria. 4th International Symposium on Modelling and Implementation of Complex Systems, 2016, 〈http://www.univ-constantine2.dz/misc2016/〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01382012
Contributeur : Mathieu Roche <>
Soumis le : samedi 15 octobre 2016 - 04:34:35
Dernière modification le : jeudi 24 mai 2018 - 15:59:25

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MISC16_v4.pdf
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  • HAL Id : lirmm-01382012, version 1

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Mathieu Roche. Knowledge Discovery from Texts on Agriculture Domain. MISC: Modelling and Implementation of Complex Systems, May 2016, Constantine, Algeria. 4th International Symposium on Modelling and Implementation of Complex Systems, 2016, 〈http://www.univ-constantine2.dz/misc2016/〉. 〈lirmm-01382012〉

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