Food packaging permeability and composition dataset dedicated to text‐mining

Abstract

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).

Partial n-Ary relation instances on food packaging composition and permeability extracted from scientific publication tables

“…All pre-processing codes used are available in the dataset. This dataset is complementary to the work presented in [6] focused on the annotation of argument instances in document texts. This previous dataset was used to evaluate approaches dealing with the extraction of argument instances from scientific documents [7] , [8] .…”