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dc.contributor.authorVan Hooland, Seth <Vrije Universiteit Brussel>
dc.contributor.authorCoeckelbergs, Mathias <Université libre de Bruxelles>
dc.date.accessioned2022-06-01T14:42:40Z
dc.date.available2022-06-01T14:42:40Z
dc.date.issued2022
dc.identifier.citationSeth van Hooland, Mathias Coeckelbergs, "Exploring Large-Scale Digital Archives – Opportunities and Limits to Use Unsupervised Machine Learning for the Extraction of Semantics", in Handbook of Digital Public History, edited by Serge Noiret, Mark Tebeau and Gerben Zaagsma, Berlin, Boston: De Gruyter Oldenbourg, 2022, pp. 517-530it_IT
dc.identifier.isbn978-3-11-043922-9it_IT
dc.identifier.isbne-ISBN: 978-3-11-043029-5
dc.identifier.urihttps://doi.org/10.1515/9783110430295-046it_IT
dc.identifier.urihttp://elea.unisa.it:8080/xmlui/handle/10556/6164
dc.identifier.urihttp://dx.doi.org/10.14273/unisa-4256
dc.description.abstractThe current excitement in regards to machine learning has spurred enthu-siasm amongst collection holders and historians alike to rely on algorithms to re-duce the amount of manual labor required for management and appraisal of largevolumes of non-structured archival content. The Digital Humanities and commer-cial archival software promote out-of-the-box tools for auto-classification, but is theadoption of machine learning as straightforward as it is currently presented in boththe popular press and the Digital Humanities literature? This chapter brings a senseof pragmatism to the debate by giving an overview of both possibilities and limitsof machine learning to extract semantics from large collections of digitized textualarchives. Two methods have gained substantial popularity: Topic Modeling (TM)and Word Embeddings (WE). This chapter introduces these non-supervised ma-chine learning methods to the community of historians, based on an experimentalcase-study of digitized archival holdings of the European Commission (EC).it_IT
dc.format.extentP. 517-530it_IT
dc.language.isoenit_IT
dc.publisherS. van Hooland, M. Coeckelbergs, "Exploring Large-Scale Digital Archives – Opportunities and Limits to Use Unsupervised Machine Learning for the Extraction of Semantics", in Handbook of Digital Public History, Berlin, Boston: De Gruyter Oldenbourg, 2022, pp. 517-530it_IT
dc.relation.ispartofDe Gruyter Referenceit_IT
dc.rightsDiritti riservati Walter de Gruyter GmbH, Berlin/Bostonit_IT
dc.sourceUniSa. Sistema Bibliotecario di Ateneoit_IT
dc.subjectMachine learningit_IT
dc.subjectMetadatait_IT
dc.subjectInformation extractionit_IT
dc.subjectBig datait_IT
dc.subjectDigital humanitiesit_IT
dc.subjectDigital archivesit_IT
dc.titleExploring Large-Scale Digital Archives – Opportunities and Limits to Use Unsupervised Machine Learning for the Extraction of Semanticsit_IT
dc.typeBook chapterit_IT
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