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dc.contributor.authorWang, Weibo Jr
dc.date.accessioned2016-04-21T12:39:38Z
dc.date.available2016-04-21T12:39:38Z
dc.identifier.urihttp://hdl.handle.net/10222/71479
dc.description.abstractTechnical writing in professional environments, such as user manual authoring, requires uniform language. Non-uniform language detection is a novel task, which aims to guarantee the consistency for technical writing by detecting sentences in a document that are intended to have the same meaning within a similar context but use different words/writing style. This thesis proposes an approach that utilizes text similarity algorithms at lexical, syntactic, semantic and pragmatic levels. Different metrics are integrated by applying a machine learning classification method. We tested our method using smart phone user manuals, and compared the performance against the state-of-the-art methods in related area. The experiments demonstrate our approach is the most efficient solution to date.en_US
dc.language.isoenen_US
dc.subjectNLPen_US
dc.subjecttext miningen_US
dc.subjectsupervised machine learningen_US
dc.titleNon-uniform Language Detection in Technical Writingen_US
dc.date.defence2016-04-06
dc.contributor.departmentFaculty of Computer Scienceen_US
dc.contributor.degreeMaster of Computer Scienceen_US
dc.contributor.external-examinern/aen_US
dc.contributor.graduate-coordinatorMalcolm Heywooden_US
dc.contributor.thesis-readerAbidalrahman Mohammaden_US
dc.contributor.thesis-readerVlado Keseljen_US
dc.contributor.thesis-supervisorEvangelos E. Miliosen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.copyright-releaseNot Applicableen_US
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