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dc.contributor.authorHollingsworth, Charles Dale
dc.date.accessioned2014-03-04T20:36:01Z
dc.date.available2014-03-04T20:36:01Z
dc.date.issued2012-08
dc.identifier.otherhollingsworth_charles_d_201208_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/hollingsworth_charles_d_201208_ms
dc.identifier.urihttp://hdl.handle.net/10724/28292
dc.description.abstractMost approaches to statistical stylometry have concentrated on lexical features, such as relative word frequencies or type-token ratios. Syntactic features have been largely ignored. This work attempts to fill that void by introducing a technique for authorship attribution based on dependency grammar. Syntactic features are extracted from texts using a common dependency parser, and those features are used to train a classifier to identify texts by author. While the method described does not outperform existing methods on most tasks, it does demonstrate that purely syntactic features carry information which could be useful for stylometric analysis.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectstylometry
dc.subjectauthorship attribution
dc.subjectdependency grammar
dc.subjectmachine learning
dc.titleSyntactic stylometry
dc.title.alternativeusing sentence structure for authorship attribution
dc.typeThesis
dc.description.degreeMS
dc.description.departmentArtificial Intelligence Center
dc.description.majorArtificial Intelligence
dc.description.advisorMichael Covington
dc.description.committeeMichael Covington
dc.description.committeeWilliam Kretzschmar
dc.description.committeeLewis Howe


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