Slicing and Dicing a Newspaper Corpus for Historical Ecology Research

Marieke van Erp, Jesse de Does, Katrien Depuydt, Rob Lenders, Thomas van Goethem

Research output: Chapter in book/volumeContribution to conference proceedingsScientificpeer-review

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Abstract

Historical newspapers are a novel source of information for historical ecologists to study the interactions between humans and animals through time and space. Newspaper archives are particularly interesting to analyse because of their breadth and depth. However, the size and the occasional noisiness of such archives also brings difficulties, as manual analysis is impossible. In this paper, we present experiments and results on automatic query expansion and categorisation for the perception of animal species between 1800 and 1940. For query expansion and to the manual annotation process, we used lexicons. For the categorisation we trained a Support Vector Machine model. Our results indicate that we can distinguish newspaper articles that are about animal species from those that are not with an F 1 of 0.92 and the subcategorisation of the different types of newspapers on animals up to 0.84 F 1 .
Original languageEnglish
Title of host publication Knowledge Engineering and Knowledge Management
PublisherSpringer Verlag GmbH
Pages470-484
Publication statusPublished - 2018

Keywords

  • Natural language processing
  • Digital libraries
  • Historical ecology
  • digital humanities
  • lexicology

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