Boosting biomedical document classification through the use of domain entity recognizers and semantic ontologies for document representation

Detalhes bibliográficos
Autor(a) principal: Pérez-Pérez, Martín
Data de Publicação: 2022
Outros Autores: Ferreira, Tânia, Lourenço, Anália, Igrejas, Gilberto, Fdez-Riverola, Florentino
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/151069
Resumo: Funding Information: SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from the University of Vigo for hosting its IT infrastructure. This work was supported by: the Associate Laboratory for Green Chemistry - LAQV financed by the Portuguese Foundation for Science and Technology (FCT/MCTES) Ref. UID/QUI/50006/2020. Ref. NORTE-01-0145-FEDER-000004; the Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group, the “Centro singular de investigación de Galicia” (accreditation 2019-2022) funded by the European Regional Development Fund (ERDF)-Ref. ED431G2019/06. The authors also acknowledge the postdoctoral fellowship [ED481B-2019-032] of Martín Pérez-Pérez, funded by Xunta de Galicia. Funding for open access charge: Universidade de Vigo/CISUG. Publisher Copyright: © 2021 The Author(s)
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spelling Boosting biomedical document classification through the use of domain entity recognizers and semantic ontologies for document representationThe case of gluten bibliomeDocument classificationGluten bibliomeLiterature miningOntology-based representationSemi-automatic curationComputer Science ApplicationsCognitive NeuroscienceArtificial IntelligenceFunding Information: SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from the University of Vigo for hosting its IT infrastructure. This work was supported by: the Associate Laboratory for Green Chemistry - LAQV financed by the Portuguese Foundation for Science and Technology (FCT/MCTES) Ref. UID/QUI/50006/2020. Ref. NORTE-01-0145-FEDER-000004; the Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group, the “Centro singular de investigación de Galicia” (accreditation 2019-2022) funded by the European Regional Development Fund (ERDF)-Ref. ED431G2019/06. The authors also acknowledge the postdoctoral fellowship [ED481B-2019-032] of Martín Pérez-Pérez, funded by Xunta de Galicia. Funding for open access charge: Universidade de Vigo/CISUG. Publisher Copyright: © 2021 The Author(s)The increasing number of scientific research documents published keeps growing at an unprecedented rate, making it increasingly difficult to access practical information within a target domain. This situation is motivating a growing interest in applying text mining techniques for the automatic processing of text resources to structure the information that helps researchers to find information of interest and infer knowledge of practical use. However, the automatic processing of research documents requires the previous existence of large, manually annotated text corpora to develop robust and accurate text mining processing methods and machine learning models. In this context, semi-automatic extraction techniques based on structured data and state-of-the-art biomedical tools appear to have significant potential to enhance curator productivity and reduce the costs of document curation. In this line, this work proposes a semi-automatic machine learning workflow and a NER + Ontology boosting technique for the automatic classification of biomedical literature. The practical relevance of the proposed approach has been proven in the curation of 4,115 gluten-related documents extracted from PubMed and contrasted against the word embedding alternative. Comparing the results of the experiments, the proposed NER + Ontology technique is an effective alternative to other state-of-the-art document representation techniques to process the existing biomedical literature.LAQV@REQUIMTERUNPérez-Pérez, MartínFerreira, TâniaLourenço, AnáliaIgrejas, GilbertoFdez-Riverola, Florentino2023-03-22T22:28:17Z2022-05-012022-05-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article15application/pdfhttp://hdl.handle.net/10362/151069eng0925-2312PURE: 56608635https://doi.org/10.1016/j.neucom.2021.10.100info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:33:30Zoai:run.unl.pt:10362/151069Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:26.588773Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Boosting biomedical document classification through the use of domain entity recognizers and semantic ontologies for document representation
The case of gluten bibliome
title Boosting biomedical document classification through the use of domain entity recognizers and semantic ontologies for document representation
spellingShingle Boosting biomedical document classification through the use of domain entity recognizers and semantic ontologies for document representation
Pérez-Pérez, Martín
Document classification
Gluten bibliome
Literature mining
Ontology-based representation
Semi-automatic curation
Computer Science Applications
Cognitive Neuroscience
Artificial Intelligence
title_short Boosting biomedical document classification through the use of domain entity recognizers and semantic ontologies for document representation
title_full Boosting biomedical document classification through the use of domain entity recognizers and semantic ontologies for document representation
title_fullStr Boosting biomedical document classification through the use of domain entity recognizers and semantic ontologies for document representation
title_full_unstemmed Boosting biomedical document classification through the use of domain entity recognizers and semantic ontologies for document representation
title_sort Boosting biomedical document classification through the use of domain entity recognizers and semantic ontologies for document representation
author Pérez-Pérez, Martín
author_facet Pérez-Pérez, Martín
Ferreira, Tânia
Lourenço, Anália
Igrejas, Gilberto
Fdez-Riverola, Florentino
author_role author
author2 Ferreira, Tânia
Lourenço, Anália
Igrejas, Gilberto
Fdez-Riverola, Florentino
author2_role author
author
author
author
dc.contributor.none.fl_str_mv LAQV@REQUIMTE
RUN
dc.contributor.author.fl_str_mv Pérez-Pérez, Martín
Ferreira, Tânia
Lourenço, Anália
Igrejas, Gilberto
Fdez-Riverola, Florentino
dc.subject.por.fl_str_mv Document classification
Gluten bibliome
Literature mining
Ontology-based representation
Semi-automatic curation
Computer Science Applications
Cognitive Neuroscience
Artificial Intelligence
topic Document classification
Gluten bibliome
Literature mining
Ontology-based representation
Semi-automatic curation
Computer Science Applications
Cognitive Neuroscience
Artificial Intelligence
description Funding Information: SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from the University of Vigo for hosting its IT infrastructure. This work was supported by: the Associate Laboratory for Green Chemistry - LAQV financed by the Portuguese Foundation for Science and Technology (FCT/MCTES) Ref. UID/QUI/50006/2020. Ref. NORTE-01-0145-FEDER-000004; the Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group, the “Centro singular de investigación de Galicia” (accreditation 2019-2022) funded by the European Regional Development Fund (ERDF)-Ref. ED431G2019/06. The authors also acknowledge the postdoctoral fellowship [ED481B-2019-032] of Martín Pérez-Pérez, funded by Xunta de Galicia. Funding for open access charge: Universidade de Vigo/CISUG. Publisher Copyright: © 2021 The Author(s)
publishDate 2022
dc.date.none.fl_str_mv 2022-05-01
2022-05-01T00:00:00Z
2023-03-22T22:28:17Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/151069
url http://hdl.handle.net/10362/151069
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0925-2312
PURE: 56608635
https://doi.org/10.1016/j.neucom.2021.10.100
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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