Towards a Classifier for Digital Sensitivity Review

  • Graham McDonald
  • Craig Macdonald
  • Iadh Ounis
  • Timothy Gollins
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8416)


The sensitivity review of government records is essential before they can be released to the official government archives, to prevent sensitive information (such as personal information, or that which is prejudicial to international relations) from being released. As records are typically reviewed and released after a period of decades, sensitivity review practices are still based on paper records. The transition to digital records brings new challenges, e.g. increased volume of digital records, making current practices impractical to use. In this paper, we describe our current work towards developing a sensitivity review classifier that can identify and prioritise potentially sensitive digital records for review. Using a test collection built from government records with real sensitivities identified by government assessors, we show that considering the entities present in each record can markedly improve upon a text classification baseline.


Personal Information International Relation Sentiment Analysis Digital Record Test Collection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Graham McDonald
    • 1
  • Craig Macdonald
    • 1
  • Iadh Ounis
    • 1
  • Timothy Gollins
    • 1
  1. 1.School of Computing ScienceUniversity of GlasgowGlasgowUK

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