Towards a classifier for digital sensitivity review

McDonald, G., Macdonald, C., Ounis, I. and Gollins, T. (2014) Towards a classifier for digital sensitivity review. Lecture Notes in Computer Science, 8416, pp. 500-506. (doi:10.1007/978-3-319-06028-6_48)

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Publisher's URL: http://dx.doi.org/10.1007/978-3-319-06028-6_48

Abstract

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.

Item Type:Articles
Additional Information:Proceedings of 36th European Conference on IR Research, ECIR 2014, Amsterdam, The Netherlands, 13-16 April, 2014. ISBN: 9783319060279
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Macdonald, Dr Craig and Ounis, Professor Iadh and Gollins, Mr Timothy
Authors: McDonald, G., Macdonald, C., Ounis, I., and Gollins, T.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Lecture Notes in Computer Science
Publisher:Springer Verlag
ISSN:0302-9743
ISSN (Online):1611-3349
Copyright Holders:Copyright © 2014 Springer
First Published:First published in Lecture Notes in Computer Science 7416:500-506
Publisher Policy:Reproduced under a Creative Commons License

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