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dc.contributor.authorSari, Y
dc.contributor.authorStevenson, M
dc.contributor.authorVlachos, Andreas
dc.date.accessioned2021-12-09T00:32:16Z
dc.date.available2021-12-09T00:32:16Z
dc.date.issued2018-01-01
dc.identifier.isbn9781948087506
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/331299
dc.description.abstractApproaches to authorship attribution, the task of identifying the author of a document, are based on analysis of individuals’ writing style and/or preferred topics. Although the problem has been widely explored, no previous studies have analysed the relationship between dataset characteristics and effectiveness of different types of features. This study carries out an analysis of four widely used datasets to explore how different types of features affect authorship attribution accuracy under varying conditions. The results of the analysis are applied to authorship attribution models based on both discrete and continuous representations. We apply the conclusions from our analysis to an extension of an existing approach to authorship attribution and outperform the prior state-of-the-art on two out of the four datasets used.
dc.publisherAssociation for Computational Linguistics
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleTopic or style? Exploring the most useful features for authorship attribution
dc.typeConference Object
dc.publisher.departmentDepartment of Computer Science And Technology
dc.date.updated2021-12-08T11:10:47Z
prism.endingPage353
prism.publicationDate2018
prism.publicationNameCOLING 2018 - 27th International Conference on Computational Linguistics, Proceedings
prism.startingPage343
dc.identifier.doi10.17863/CAM.78746
rioxxterms.versionofrecord10.17863/CAM.78746
rioxxterms.versionVoR
dc.contributor.orcidVlachos, Andreas [0000-0003-2123-5071]
dc.publisher.urlhttps://aclanthology.org/C18-1029/
pubs.conference-nameThe 27th International Conference on Computational Linguistics
pubs.conference-start-date2018-08-20
cam.orpheus.success2022-05-11: embargo success field applied
cam.orpheus.counter6
cam.depositDate2021-12-08
pubs.conference-finish-date2018-08-26
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International