SciPuRe: a new Representation of textual data for entity identification from scientific publications

Lentschat Martin, Dibie-Barthélemy Juliette, Buche Patrice, Roche Mathieu. 2020. SciPuRe: a new Representation of textual data for entity identification from scientific publications. In : WIMIS 2020: 10th International Conference on Web Intelligence, Mining and Semantics (WIMS 20). ACM, SIGAPP.fr, Université de Pau et des Pays de l'Adour, IUT Bayonne Pays Basque. New York : ACM, 220-226. International Conference on Wev Intelligence, Mining and Semant (WIMS 20). 10, Biarritz, France, 30 Juin 2020/3 Juillet 2020.

Résumé : Retrieving entities associated with experimental data in the textual content of scientific documents faces numbers of challenges. One of them is the assessment of the extracted entities for further process, especially the identification of false positives. We present in this paper SciPuRe (Scientific Publication Representation): a new representation of entities. The extraction process presented in this paper is driven by an Ontological and Terminological Resource (OTR). It is applied to the extraction of entities associated with food packaging permeabilities, that can be symbolic (e.g. the Packaging "low density polyethylene") or quantitative (e.g. the Temperature "25", "◦

Mots-clés libres : Text Mining, Ontological and Terminological Resource, Information retrieval, Information extraction

Auteurs et affiliations

• Lentschat Martin, CIRAD-ES-UMR TETIS (FRA)
• Dibie-Barthélemy Juliette, AgroParisTech (FRA)
• Buche Patrice, INRAE (FRA)
• Roche Mathieu, CIRAD-ES-UMR TETIS (FRA) ORCID: 0000-0003-3272-8568

Source : Cirad-Agritrop (https://agritrop.cirad.fr/596152/)

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