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

This dataset is dedicated to text mining and is composed of partial n-Ary relation instances concerning food packaging composition and gas permeability. It was created from 31 tables derived from 10 English-language scientific articles in html format from several international journals hosted on the...

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Detalles Bibliográficos
Autores principales: Lentschat, Martin, Buche, Patrice, Menut, Luc, Guari, Romane, Roche, Mathieu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8919229/
https://www.ncbi.nlm.nih.gov/pubmed/35295868
http://dx.doi.org/10.1016/j.dib.2022.108000
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author Lentschat, Martin
Buche, Patrice
Menut, Luc
Guari, Romane
Roche, Mathieu
author_facet Lentschat, Martin
Buche, Patrice
Menut, Luc
Guari, Romane
Roche, Mathieu
author_sort Lentschat, Martin
collection PubMed
description This dataset is dedicated to text mining and is composed of partial n-Ary relation instances concerning food packaging composition and gas permeability. It was created from 31 tables derived from 10 English-language scientific articles in html format from several international journals hosted on the ScienceDirect website. This dataset includes two sets of data: manual table annotation results and automatic data extraction results. The tables were first annotated by one annotator and cross-curated by three different annotators. The annotation task aimed to identify all table data dealing with packaging permeability measurements and compositions. An Ontological and Terminological Resource (OTR) was used for the annotation process. The annotation guidelines were drawn up through a collective iterative approach involving the annotators, and they may be accessed alongside the data. This dataset of n-Ary relations can be used in natural language processing (NLP) approaches implemented in experimental fields, especially for n-Ary relation extraction research. It can also be useful for training or evaluation of methods for the extraction of experimental data from tables and text in scientific documents, especially in experimental domains such as food packaging.
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spelling pubmed-89192292022-03-15 Partial n-Ary relation instances on food packaging composition and permeability extracted from scientific publication tables() Lentschat, Martin Buche, Patrice Menut, Luc Guari, Romane Roche, Mathieu Data Brief Data Article This dataset is dedicated to text mining and is composed of partial n-Ary relation instances concerning food packaging composition and gas permeability. It was created from 31 tables derived from 10 English-language scientific articles in html format from several international journals hosted on the ScienceDirect website. This dataset includes two sets of data: manual table annotation results and automatic data extraction results. The tables were first annotated by one annotator and cross-curated by three different annotators. The annotation task aimed to identify all table data dealing with packaging permeability measurements and compositions. An Ontological and Terminological Resource (OTR) was used for the annotation process. The annotation guidelines were drawn up through a collective iterative approach involving the annotators, and they may be accessed alongside the data. This dataset of n-Ary relations can be used in natural language processing (NLP) approaches implemented in experimental fields, especially for n-Ary relation extraction research. It can also be useful for training or evaluation of methods for the extraction of experimental data from tables and text in scientific documents, especially in experimental domains such as food packaging. Elsevier 2022-03-02 /pmc/articles/PMC8919229/ /pubmed/35295868 http://dx.doi.org/10.1016/j.dib.2022.108000 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Data Article
Lentschat, Martin
Buche, Patrice
Menut, Luc
Guari, Romane
Roche, Mathieu
Partial n-Ary relation instances on food packaging composition and permeability extracted from scientific publication tables()
title Partial n-Ary relation instances on food packaging composition and permeability extracted from scientific publication tables()
title_full Partial n-Ary relation instances on food packaging composition and permeability extracted from scientific publication tables()
title_fullStr Partial n-Ary relation instances on food packaging composition and permeability extracted from scientific publication tables()
title_full_unstemmed Partial n-Ary relation instances on food packaging composition and permeability extracted from scientific publication tables()
title_short Partial n-Ary relation instances on food packaging composition and permeability extracted from scientific publication tables()
title_sort partial n-ary relation instances on food packaging composition and permeability extracted from scientific publication tables()
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8919229/
https://www.ncbi.nlm.nih.gov/pubmed/35295868
http://dx.doi.org/10.1016/j.dib.2022.108000
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