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dc.contributor.authorMa, Kai
dc.description.abstractQuestion Answering (QA) communities such as Yahoo! Answers and Baidu Zhidao are currently very popular with millions of users. The QA communities are particularly useful for the educational domain such as mathematics. Similar to traditional QA communities, a mathematical QA community should also allow users to search, ask, answer and discover mathematical questions. However, as mathematical formulas are highly symbolic and structured, it is challenging to develop such a mathematical QA community. In this research, we aim to propose efficient and effective techniques for supporting the ”search, ask, answer and discover” framework for a mathematical QA community. In particular, we focus on investigating different data mining techniques for mathematical question search, mathematical question topic classification and human expert finding. Mathematical question search will help retrieve a set of similar mathematical problems together with the answers posted by other users. Mathematical question topic classification will help recommend the possible topics of user posted questions. Human expert finding will help find a list of experts who are most likely able to answer a posted question according to their expertise.en_US
dc.format.extent136 p.en_US
dc.subjectDRNTU::Engineering::Computer science and engineering::Information systems::Database managementen_US
dc.titleData mining for mathematical question answering communityen_US
dc.contributor.supervisorHui Siu Cheungen_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.description.degreeCOMPUTER ENGINEERINGen_US
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