Food packaging permeability and composition dataset dedicated to text-mining

Lentschat Martin, Buche Patrice, Dibie-Barthélemy Juliette, Menut Luc, Roche Mathieu. 2021. Food packaging permeability and composition dataset dedicated to text-mining. Data in Brief, 36:107135, 6 p.

https://doi.org/10.1016/j.dib.2021.107135

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Url - jeu de données - Dataverse Cirad : https://doi.org/10.18167/DVN1/U7HK8J

Résumé : This dataset is composed of symbolic and quantitative entities concerning food packaging composition and gas permeability. It was created from 50 scientific articles in English registered in html format from several international journals on the ScienceDirect website. The files were annotated independently by three experts on a WebAnno server. The aim of the annotation task was to recognize all entities related to packaging permeability measures and packaging composition. This annotation task is driven by an Ontological and Terminological Resource (OTR). An annotation guideline was designed in a collective and iterative approach involving the annotators. This dataset can be used to train or evaluate natural language processing (NLP) approaches in experimental fields, such as specialized entity recognition (e.g. terms and variations, units of measure, complex numerical values) or sentence level binary relation (e.g. value to unit, term to acronym).

Mots-clés Agrovoc : fouille de textes, fouille de données, ontologie, analyse de données, conditionnement des aliments, perméabilité

Mots-clés complémentaires : traitement automatique des langues

Mots-clés libres : Natural language processing, Text Mining, Ontology, Food packaging, Permeability

Classification Agris : C30 - Documentation et information
U10 - Informatique, mathématiques et statistiques
Q80 - Conditionnement

Champ stratégique Cirad : CTS 3 (2019-) - Systèmes alimentaires

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