Typifying Wikipedia Articles

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Typifying Wikipedia Articles

Show simple item record Hasan, Quazi Mainul en_US 2011-03-03T21:51:34Z 2011-03-03T21:51:34Z 2011-03-03 January 2010 en_US
dc.identifier.other DISS-10927 en_US
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 Computer Science & Engineering en_US Computer Science & Engineering en_US University of Texas at Arlington en_US masters en_US M.S. en_US

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