Knowledge discovery from texts on agriculture domain

Roche Mathieu. 2016. Knowledge discovery from texts on agriculture domain. In : Proceedings of International Symposium on Modelling and Implementation of Complex Systems. Université de Constantine 2. Constantine : Université de Constantine 2, 47 p. MISC 2016 : International Symposium on Modelling and Implementation of Complex Systems. 4, Constantine, Algérie, 7 Mai 2016/8 Mai 2016.

Résumé : 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.

Mots-clés libres : Natural language processing, Text mining, Web mining, Information retrieval, Information extraction, Terminology extraction, Word sense disambiguation, AgroNLP

Classification Agris : C30 - Documentation et information
U10 - Informatique, mathématiques et statistiques
U30 - Méthodes de recherche
A01 - Agriculture - Considérations générales

Auteurs et affiliations

• Roche Mathieu, CIRAD-ES-UMR TETIS (FRA) ORCID: 0000-0003-3272-8568

Source : Cirad-Agritrop (https://agritrop.cirad.fr/580747/)

Métriques

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