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Discovering types of spatial relations with a text mining approach

Zenasni Sarah, Kergosien Eric, Roche Mathieu, Teisseire Maguelonne. 2015. Discovering types of spatial relations with a text mining approach. In : Foundations of intelligent systems. Esposito Floriana (ed.), Pivert Olivier (ed.), Hacid Mohand-Said (ed.), Rás Zbigniew W. (ed.), Ferilli Stefano (ed.). Cham : Springer International Publishing, pp. 442-451. (Lecture Notes in Computer Science, 9384) ISBN 978-3-319-25252-0 International Symposium on Methodologies for Intelligent Systems. 22, Lyon, France, 21 October 2015/23 October 2015.

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Abstract : Knowledge discovery from texts, particularly the identification of spatial information is a difficult task due to the complexity of texts written in natural language. Here we propose a method combining two statistical approaches (lexical and contextual analysis) and a text mining approach to automatically identify types of spatial relations. Experiments conducted on an English corpus are presented. (Résumé d'auteur)

Mots-clés libres : Spatial relations, Text mining

Classification Agris : C30 - Documentation and information
U10 - Computer science, mathematics and statistics

Auteurs et affiliations

  • Zenasni Sarah, CIRAD-ES-UMR TETIS (FRA)
  • Kergosien Eric, Université de Lille (FRA)
  • Roche Mathieu, CIRAD-ES-UMR TETIS (FRA) ORCID: 0000-0003-3272-8568
  • Teisseire Maguelonne, LIRMM (FRA)

Autres liens de la publication

Source : Cirad-Agritrop (https://agritrop.cirad.fr/579646/)

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