dc.contributor.author | Wang, Weibo Jr | |
dc.date.accessioned | 2016-04-21T12:39:38Z | |
dc.date.available | 2016-04-21T12:39:38Z | |
dc.date.issued | 2016-04-21T12:39:38Z | |
dc.identifier.uri | http://hdl.handle.net/10222/71479 | |
dc.description.abstract | Technical 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.iso | en | en_US |
dc.subject | NLP | en_US |
dc.subject | text mining | en_US |
dc.subject | supervised machine learning | en_US |
dc.title | Non-uniform Language Detection in Technical Writing | en_US |
dc.date.defence | 2016-04-06 | |
dc.contributor.department | Faculty of Computer Science | en_US |
dc.contributor.degree | Master of Computer Science | en_US |
dc.contributor.external-examiner | n/a | en_US |
dc.contributor.graduate-coordinator | Malcolm Heywood | en_US |
dc.contributor.thesis-reader | Abidalrahman Mohammad | en_US |
dc.contributor.thesis-reader | Vlado Keselj | en_US |
dc.contributor.thesis-supervisor | Evangelos E. Milios | en_US |
dc.contributor.ethics-approval | Not Applicable | en_US |
dc.contributor.manuscripts | Not Applicable | en_US |
dc.contributor.copyright-release | Not Applicable | en_US |