Genomics & Informatics

  • 제18권2호
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  • Pages.24.1-24.7
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  • 2020
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  • 1598-866X(pISSN)
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  • 2234-0742(eISSN)
한국유전체학회 (Korea Genome Organization)
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An empirical evaluation of electronic annotation tools for Twitter data

  • Weissenbacher, Davy (Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania) ;
  • O'Connor, Karen (Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania) ;
  • Hiraki, Aiko T. (Database Center for Life Science, Research Organization of Information and Systems) ;
  • Kim, Jin-Dong (Database Center for Life Science, Research Organization of Information and Systems) ;
  • Gonzalez-Hernandez, Graciela (Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania)
  • 투고 : 2020.03.29
  • 심사 : 2020.06.16
  • 발행 : 2020.05.28
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초록

Despite a growing number of natural language processing shared-tasks dedicated to the use of Twitter data, there is currently no ad-hoc annotation tool for the purpose. During the 6th edition of Biomedical Linked Annotation Hackathon (BLAH), after a short review of 19 generic annotation tools, we adapted GATE and TextAE for annotating Twitter timelines. Although none of the tools reviewed allow the annotation of all information inherent of Twitter timelines, a few may be suitable provided the willingness by annotators to compromise on some functionality.

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참고문헌

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