Extending defoe for the efficient analysis of historical texts at scale

Rosa Filgueira, Beatrice Alex, Claire Grover, Vasileios Karaiskos, Sarah Van Eyndhoven, Lisa Gotthard, Melissa Terras

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This 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.
Original languageEnglish
Title of host publicationIEEE eScience 2021
Publication statusAccepted/In press - 30 Jun 2021
EventIEEE eScience 2021 - 17th IEEE eScience 2021 International Conference - University of Innsbruck , Innsbruck, Austria
Duration: 20 Sep 202123 Sep 2021
https://www.escience2021.org

Conference

ConferenceIEEE eScience 2021 - 17th IEEE eScience 2021 International Conference
Abbreviated titleeScience 2021
CountryAustria
CityInnsbruck
Period20/09/2123/09/21
Internet address

Keywords

  • text mining
  • distributed queries
  • High Performance Computing
  • XML schemas
  • digital tools
  • humanities research

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