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Protecting Privacy in the Archives: Preliminary Explorations of Topic Modeling for Born-Digital Collections

dc.contributor.authorHutchinson, Tim
dc.date.accessioned2018-06-22T19:24:02Z
dc.date.available2018-06-22T19:24:02Z
dc.date.issued2017-12
dc.description.abstractNatural language processing (NLP) is an area of increased interest for digital archivists, although most research to date has focused on digitized rather than born-digital collections. This study in progress explores whether NLP techniques can be used effectively to surface documents requiring restrictions due to their personal information content. This phase of the research focuses on using topic modeling to find records relating to human resources. Early results show some promise, but suggest that topic modeling on its own will not be sufficient; other techniques to be explored include sentiment analysis and named entity extraction.en_US
dc.identifier.citationTim Hutchinson, 2017. Protecting Privacy in the Archives: Preliminary Explorations of Topic Modeling for Born-Digital Collections. Proceedings of the 2017 IEEE International Conference on Big Data. Boston, MA: 11-14 December 2017, pp. 2251-2255.en_US
dc.identifier.urihttp://hdl.handle.net/10388/8625
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.titleProtecting Privacy in the Archives: Preliminary Explorations of Topic Modeling for Born-Digital Collectionsen_US
dc.typeConference Presentationen_US
dc.typeRefereed Paperen_US

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