Natural language processing and machine learning in the categorization of scientific papers: a study around ?cultural heritage?

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Estadual de Londrina (UEL)-
Autor(es): dc.creatorJesus, Ananda Fernanda de-
Autor(es): dc.creatorTriques, Maria Ligia-
Autor(es): dc.creatorSegundo, Jose Eduardo Santarem-
Autor(es): dc.creatorAlbuquerque, Ana Cristina de-
Data de aceite: dc.date.accessioned2025-08-21T23:02:01Z-
Data de disponibilização: dc.date.available2025-08-21T23:02:01Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.26512/rici.v16.n1.2023.47537-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/245641-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/245641-
Descrição: dc.descriptionAims to verify the potential of applying Natural Language Processing (NLP) and Machine Learning (ML) techniques in the thematic categorization of scientific articles on the theme cultural heritage from two situations in which categories are established a priori and later. Applied research is developed, with quantitative and qualitative results, where the first corpus consisting of scientific articles in Portuguese, on a thematic basis of Information Science, manually selected and categorized; and the second corpus, composed of scientific articles in English retrieved from the Web of Science, automatically categorized by search strategies and application of Booleans. Both were submitted to two categorization test procedures (supervised and unsupervised algorithm). The results show that in both, the participation of the researcher is essential in defining the representativeness of the chosen sample, and this has an impact on the precision and accuracy of the applied algorithms. The importance of detailing and rigor in the pre-processing of data and sample size is highlighted, however, it is emphasized that, in the case of this study, only a larger volume of data did not guarantee that the results were representative from the point of view of the domain studied, which warns that there are always multidisciplinary discussions and analyzes that allow verifying and readjusting the sample parameters.-
Descrição: dc.descriptionUniv Estadual Paulista, Programa Posgrad Ciencia Informacao, Marilia, SP, Brazil-
Descrição: dc.descriptionUniv Estadual Londrina, Programa Posgrad Ciencia Informacao, Londrina, PR, Brazil-
Descrição: dc.descriptionUniv Estadual Paulista, Programa Posgrad Ciencia Informacao, Marilia, SP, Brazil-
Formato: dc.format167-184-
Idioma: dc.languageen-
Publicador: dc.publisherUniv Brasilia, Dept Ciencia Informacao-
Relação: dc.relationRevista Ibero-americana De Ciencia Da Informacao-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectNatural language processing-
Palavras-chave: dc.subjectNeural network algorithm-
Palavras-chave: dc.subjectCultural heritage-
Palavras-chave: dc.subjectHierarchical clustering algorithm-
Título: dc.titleNatural language processing and machine learning in the categorization of scientific papers: a study around ?cultural heritage?-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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