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dc.contributor.authorSarwar, Raheem
dc.contributor.authorYu, Chenyun
dc.contributor.authorNutanong, Sarana
dc.contributor.authorUrailertprasert, Norawit
dc.contributor.authorVannaboot, Nattapol
dc.contributor.authorRakthanmanon, Thanawin
dc.contributor.editorPei, Jian
dc.contributor.editorManolopoulos, Yannis
dc.contributor.editorSadiq, Shazia W
dc.contributor.editorLi, Jianxin
dc.date.accessioned2020-10-12T09:00:23Z
dc.date.available2020-10-12T09:00:23Z
dc.date.issued2018-05-13
dc.identifier.citationSarwar R., Yu C., Nutanong S., Urailertprasert N., Vannaboot N., Rakthanmanon T. (2018) A Scalable Framework for Stylometric Analysis of Multi-author Documents. In: Pei J., Manolopoulos Y., Sadiq S., Li J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science, vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_52en
dc.identifier.isbn9783319914510en
dc.identifier.issn0302-9743en
dc.identifier.doi10.1007/978-3-319-91452-7_52en
dc.identifier.urihttp://hdl.handle.net/2436/623703
dc.descriptionThis is an accepted manuscript of a chapter published by Springer in Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science, vol 10827 on 13/05/2018, available online: https://doi.org/10.1007/978-3-319-91452-7_52 The accepted version of the publication may differ from the final published version.en
dc.description.abstractStylometry is a statistical technique used to analyze the variations in the author’s writing styles and is typically applied to authorship attribution problems. In this investigation, we apply stylometry to authorship identification of multi-author documents (AIMD) task. We propose an AIMD technique called Co-Authorship Graph (CAG) which can be used to collaboratively attribute different portions of documents to different authors belonging to the same community. Based on CAG, we propose a novel AIMD solution which (i) significantly outperforms the existing state-of-the-art solution; (ii) can effectively handle a larger number of co-authors; and (iii) is capable of handling the case when some of the listed co-authors have not contributed to the document as a writer. We conducted an extensive experimental study to compare the proposed solution and the best existing AIMD method using real and synthetic datasets. We show that the proposed solution significantly outperforms existing state-of-the-art method.en
dc.formatapplication/pdfen
dc.language.isoenen
dc.publisherSpringeren
dc.relation.ispartofseriesLecture Notes in Computer Scienceen
dc.relation.urlhttps://link.springer.com/chapter/10.1007%2F978-3-319-91452-7_52en
dc.subjectauthorship identificationen
dc.subjectco-authorship graphen
dc.subjectmulti-author documentsen
dc.subjectstylometryen
dc.titleA scalable framework for stylometric analysis of multi-author documentsen
dc.typeConference contributionen
dc.identifier.journalDatabase Systems for Advanced Applications - 23rd International Conference, DASFAA 2018, Gold Coast, QLD, Australia, May 21-24, 2018, Proceedings, Part Ien
dc.date.updated2020-10-07T19:10:00Z
dc.conference.nameDatabase Systems for Advanced Applications
dc.date.accepted2018-02-13
rioxxterms.funderCity University of Hong Kongen
rioxxterms.identifier.projectUOW12102020RSen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
rioxxterms.licenseref.startdate2020-10-12en
dc.source.volume10827
dc.source.beginpage813
dc.source.endpage829
refterms.dateFCD2020-10-12T08:57:13Z
refterms.versionFCDAM
refterms.dateFOA2020-10-12T09:00:26Z


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