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dc.contributor.authorFilgueira, Rosa
dc.contributor.authorGrover, Claire
dc.contributor.authorKaraiskos, Vasilios
dc.contributor.authorAlex, Beatrice
dc.contributor.authorVan Eyndhoven, Sarah
dc.contributor.authorGotthard, Lisa
dc.contributor.authorTerras, Melissa
dc.date.accessioned2022-02-10T16:30:01Z
dc.date.available2022-02-10T16:30:01Z
dc.date.issued2021-10-26
dc.identifier277807290
dc.identifiera1d49da2-a02e-41db-9371-d2685eb0426e
dc.identifier85119072602
dc.identifier.citationFilgueira , R , Grover , C , Karaiskos , V , Alex , B , Van Eyndhoven , S , Gotthard , L & Terras , M 2021 , Extending defoe for the efficient analysis of historical texts at scale . in 17th IEEE International Conference on eScience 2021 . IEEE International Conference on eScience , IEEE , United States , pp. 21-29 , 17th IEEE International Conference on eScience 2021 , Innsbruck , Austria , 20/09/21 . https://doi.org/10.1109/eScience51609.2021.00012en
dc.identifier.citationconferenceen
dc.identifier.isbn9781665447089
dc.identifier.isbn9781665403610
dc.identifier.issn2325-3703
dc.identifier.otherBibtex: b658ffe64e1b4ae5b2cf684014476d20
dc.identifier.urihttps://hdl.handle.net/10023/24848
dc.descriptionFunding: This work was partly funded by the Data-Driven Innovation Programme as part of the Edinburgh and South East Scotland City Region Deal, by the University of Edinburgh, and by Google Cloud Platform research credits program.en
dc.description.abstractThis paper presents the new facilities provided in defoe, a parallel toolbox for querying a wealth of digitised newspapers and books at scale. defoe has been extended to work with further Natural Language Processing () tools such as the Edinburgh Geoparser, to store the preprocessed text in several storage facilities and to support different types of queries and analyses. We have also extended the collection of XML schemas supported by defoe, increasing the versatility of the tool for the analysis of digital historical textual data at scale. Finally, we have conducted several studies in which we worked with humanities and social science researchers who posed complex and interested questions to large-scale digital collections. Results shows that defoe allows researchers to conduct their studies and obtain results faster, while all the large-scale text mining complexity is automatically handled by defoe.
dc.format.extent9
dc.format.extent8042178
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartof17th IEEE International Conference on eScience 2021en
dc.relation.ispartofseriesIEEE International Conference on eScienceen
dc.subjectDigital toolsen
dc.subjectDigitised primary historical sourcesen
dc.subjectDistributed queriesen
dc.subjectHigh-Performance Computingen
dc.subjectHumanities researchen
dc.subjectText miningen
dc.subjectXML schemasen
dc.subjectC Auxiliary sciences of history (General)en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectNSen
dc.subject.lccC1en
dc.subject.lccQA75en
dc.titleExtending defoe for the efficient analysis of historical texts at scaleen
dc.typeConference itemen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.identifier.doi10.1109/eScience51609.2021.00012
dc.identifier.urlhttps://www.research.ed.ac.uk/en/publications/extending-defoe-for-the-efficient-analysis-of-historical-texts-aten


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