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Probabilistic Modeling in Community-based Question Answering Services

dc.contributor.authorZolaktaf Zadeh, Zeinab
dc.contributor.copyright-releaseNot Applicableen_US
dc.contributor.degreeMaster of Computer Scienceen_US
dc.contributor.departmentFaculty of Computer Scienceen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.external-examinerN/Aen_US
dc.contributor.graduate-coordinatorDr Qigang Gaoen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.thesis-readerDr Axel Sotoen_US
dc.contributor.thesis-readerDr Mahdi Shafieien_US
dc.contributor.thesis-supervisorDr Evangelos Miliosen_US
dc.date.accessioned2012-04-03T18:55:49Z
dc.date.available2012-04-03T18:55:49Z
dc.date.defence2012-02-29
dc.date.issued2012-04-03
dc.description.abstractCommunity-based Question Answering (CQA) services enable members to ask questions and have them answered by the community. These services have the potential of rapidly creating large archives of questions and answers. However, their information is rarely exploited. This thesis presents a new statistical topic model for modeling Question-Answering archives. The model explicitly captures topic dependency and correlation between questions and answers, and models differences in their vocabulary. The proposed model is applied for the task of Question Answering and its performance is evaluated using a dataset extracted from the programming website Stack Overflow. Experimental results show that it achieves improved performance in retrieving the correct answer for a query question compared to the LDA model. The model has also been applied for Automatic Tagging and comparisons with LDA show that the new model achieves better clustering performance for larger numbers of topics.en_US
dc.identifier.urihttp://hdl.handle.net/10222/14584
dc.language.isoenen_US
dc.subjectProbabilistic Modeling, Community-based Question Answering Services, Automatic Tagging, Question Answeringen_US
dc.titleProbabilistic Modeling in Community-based Question Answering Servicesen_US

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