Machine Learning for Technical Information Quality Assessment
Ladda ner
Typ
Examensarbete för masterexamen
Master Thesis
Master Thesis
Program
Computer science – algorithms, languages and logic (MPALG), MSc
Publicerad
2016
Författare
Andersson, Emil
Englund, Rickard
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
Data- och informationsvetenskap , Computer and Information Science