Machine Learning for Technical Information Quality Assessment

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/234989
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Type: Examensarbete för masterexamen
Master Thesis
Title: Machine Learning for Technical Information Quality Assessment
Authors: Andersson, Emil
Englund, Rickard
Abstract: This 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.
Keywords: Data- och informationsvetenskap;Computer and Information Science
Issue Date: 2016
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
URI: https://hdl.handle.net/20.500.12380/234989
Collection:Examensarbeten för masterexamen // Master Theses



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