Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/73504

TítuloNER in Archival Finding Aids
Autor(es)Cunha, Luís Filipe da Costa
Ramalho, José Carlos
Palavras-chaveNamed Entity Recognition
Archival Descriptions
Machine Learning
Deep Learning
Data1-Jul-2021
EditoraSchloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH
RevistaOASIcs: OpenAccess Series in Informatics
Resumo(s)At the moment, the vast majority of Portuguese archives with an online presence use a software solution to manage their finding aids: e. g. Digitarq or Archeevo. Most of these finding aids are written in natural language without any annotation that would enable a machine to identify named entities, geographical locations or even some dates. That would allow the machine to create smart browsing tools on top of those record contents like entity linking and record linking. In this work we have created a set of datasets to train Machine Learning algorithms to find those named entities and geographical locations. After training several algorithms we tested them in several datasets and registered their precision and accuracy. These results enabled us to achieve some conclusions about what kind of precision we can achieve with this approach in this context and what to do with the results: do we have enough precision and accuracy to create toponymic and anthroponomic indexes for archival finding aids? Is this approach suitable in this context? These are some of the questions we intend to answer along this paper.
TipoArtigo em ata de conferência
URIhttps://hdl.handle.net/1822/73504
ISBN9783959772020
DOI10.4230/OASIcs.SLATE.2021.8
ISSN2190-6807
Versão da editorahttps://drops.dagstuhl.de/opus/volltexte/2021/14425/
Arbitragem científicayes
AcessoAcesso aberto
Aparece nas coleções:CCTC - Artigos em atas de conferências internacionais (texto completo)

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NER_SLATE 2021 Presentation.pdfPresentation slides764,67 kBAdobe PDFVer/Abrir
SLATE_2021_NER_in_Archival_Finding_Aids.pdfArticle1,36 MBAdobe PDFVer/Abrir

Este trabalho está licenciado sob uma Licença Creative Commons Creative Commons

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