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Typifying Wikipedia Articles

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Typifying Wikipedia Articles

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dc.contributor.author Hasan, Quazi Mainul en_US
dc.date.accessioned 2011-03-03T21:51:34Z
dc.date.available 2011-03-03T21:51:34Z
dc.date.issued 2011-03-03
dc.date.submitted January 2010 en_US
dc.identifier.other DISS-10927 en_US
dc.identifier.uri http://hdl.handle.net/10106/5428
dc.description.abstract In Wikipedia, each article represents an entity. Entity can have different types like person, country, school, science etc. Although Wikipedia encapsulates category information for each page, sometimes it is not sufficient to deduce the type of a page just from its categories. But, incorporating the clear type information in a Wikipedia page is very important for the users, as it will help them to explore the pages in more organized way. Hence, in my thesis, we explore different standard classification techniques, mainly Naïve Bayes and Support Vector Machines and experiment how these techniques can be made more effective for typifying Wikipedia articles by using different feature selection methods. We proposed a method where Wikipedia categories are used as features. Moreover, we combine features to build a meta classifier which outperforms the other standard methods. To compare our methods we calculate the accuracy of different methods and used well known data mining tool "WEKA". en_US
dc.description.sponsorship Li, Chengkai en_US
dc.language.iso en en_US
dc.publisher Computer Science & Engineering en_US
dc.title Typifying Wikipedia Articles en_US
dc.type M.S. en_US
dc.contributor.committeeChair Li, Chengkai en_US
dc.degree.department Computer Science & Engineering en_US
dc.degree.discipline Computer Science & Engineering en_US
dc.degree.grantor University of Texas at Arlington en_US
dc.degree.level masters en_US
dc.degree.name M.S. en_US

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