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The Application of Closed Frequent Subtrees to Authorship Attribution
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2014 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

In this experimental study we compare the authorship attribution performance of two different types of distinguishing features; overlapping syntax subtrees of height one (or small trees) and closed frequent syntax subtrees. Authors and documents used in the experiments are randomly drawn from a large corpus of blog posts and news articles. Results show that small trees outperform closed frequent trees on this data set, both in terms of classifier performance and computational eciency.

Place, publisher, year, edition, pages
2014.
Series
UMNAD, 981
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:umu:diva-86458OAI: oai:DiVA.org:umu-86458DiVA: diva2:699270
Educational program
Bachelor of Science Programme in Computing Science
Supervisors
Examiners
Available from: 2014-02-27 Created: 2014-02-27 Last updated: 2014-02-27Bibliographically approved

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Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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Language
  • de-DE
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Output format
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