Machine Learning for Technical Information Quality Assessment

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dc.contributor.authorAndersson, Emil
dc.contributor.authorEnglund, Rickard
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.date.accessioned2019-07-03T13:52:55Z
dc.date.available2019-07-03T13:52:55Z
dc.date.issued2016
dc.description.abstractThis thesis is about assessing the quality of technical texts such as user manuals and product speci cations. This is done by consulting industry standards and guidelines, and implementing an automatic extractor for features describing the texts, based on these guidelines. These features are then put together into models, which are evaluated by using supervised machine learning algorithms on graded job application tests. Our conclusion is that it is probable that we can use this method and some of the features to classify the quality of technical texts. However, we think that it is hard to draw any con dent conclusions using this small data set and suggest as future work to evaluate this on a larger data set.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/234989
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectData- och informationsvetenskap
dc.subjectComputer and Information Science
dc.titleMachine Learning for Technical Information Quality Assessment
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComputer science – algorithms, languages and logic (MPALG), MSc
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