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

dc.contributor.author	Hutchinson, Tim
dc.date.accessioned	2018-06-22T19:24:02Z
dc.date.available	2018-06-22T19:24:02Z
dc.date.issued	2017-12
dc.description.abstract	Natural 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.citation	Tim 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.uri	http://hdl.handle.net/10388/8625
dc.language.iso	en	en_US
dc.publisher	IEEE	en_US
dc.title	Protecting Privacy in the Archives: Preliminary Explorations of Topic Modeling for Born-Digital Collections	en_US
dc.type	Conference Presentation	en_US
dc.type	Refereed Paper	en_US

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