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.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01382012
Contributor : Mathieu Roche <>
Submitted on : Saturday, October 15, 2016 - 4:34:35 AM
Last modification on : Wednesday, September 18, 2019 - 4:04:04 PM

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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. ⟨lirmm-01382012⟩

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