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

Martin Lentschat (1) , Juliette Dibie-Barthelemy (2) , Patrice Buche (3) , Mathieu Roche (1, 4)

1 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale
2 MIA Paris-Saclay - Mathématiques et Informatique Appliquées
3 UMR IATE - Ingénierie des Agro-polymères et Technologies Émergentes
4 Cirad-ES - Département Environnements et Sociétés

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: Text Mining; Ontological and Terminological Resource; Information retrieval; Information extraction

Domaines: Informatique [cs]; Intelligence artificielle [cs.AI]

Martin Lentschat, Juliette Dibie-Barthelemy, Patrice Buche, Mathieu Roche. SciPuRe: a new Representation of textual data for entity identification from scientific publications. International conference on Web-Intelligence, Mining and Semantics, Jun 2020, Biarritz, France. ⟨hal-02911678⟩