Extending defoe for the Efficient Analysis of Historical Texts at Scale

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

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

2 Citations (Scopus)
125 Downloads (Pure)


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 publication17th IEEE International Conference on eScience 2021
Number of pages9
ISBN (Electronic)9781665403610
Publication statusPublished - 26 Oct 2021
Event17th IEEE eScience 2021
- online
Duration: 20 Sept 202123 Sept 2021


Conference17th IEEE eScience 2021
Internet address


  • Digital tools
  • Digitised primary historical sources
  • Distributed queries
  • High-Performance Computing
  • Humanities research
  • Text mining
  • XML schemas

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality


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