An Exploration of Representation Learning and Sequential Modeling Approaches for Supervised Topic Classification in Job Advertisements

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.advisorMathioudakis, Michael
dc.contributor.authorWestrup, Clemens
dc.contributor.schoolPerustieteiden korkeakoulufi
dc.contributor.supervisorGionis, Aristides
dc.date.accessioned2016-11-02T09:46:44Z
dc.date.available2016-11-02T09:46:44Z
dc.date.issued2016-10-27
dc.description.abstractThis thesis applies the explorative double diamond design process borrowed to iteratively frame a research problem applicable in the context of a recruitment web service and then find the best approach to solve it. Thereby the problem focus is laid on multi-class classification, in particular the task of labelling sentences in job advertisements with one of six topics which were found to be covered in every typical job description. A dataset is obtained for evaluation and conventional N-Gram Vector Space models are compared with Representation Learning approaches, notably continuous distributed representations, and Sequential Modeling techniques using Recurrent Neural Networks. Results of the experiments show that the Representation Learning and Sequential Modeling approaches perform on par or better than traditional feature engineering methods and show a promising direction in and beyond research in Computational Linguistics and Natural Language Processing.en
dc.format.extent94+9
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/23375
dc.identifier.urnURN:NBN:fi:aalto-201611025476
dc.language.isoenen
dc.programmeMaster’s Programme in Machine Learning and Data Mining (Macadamia)fi
dc.programme.majorMachine Learning and Data Miningfi
dc.programme.mcodeSCI3015fi
dc.rights.accesslevelopenAccess
dc.subject.keywordnatural language processingen
dc.subject.keywordcomputational linguisticsen
dc.subject.keywordrepresentation learningen
dc.subject.keywordsequential text modelingen
dc.subject.keywordtext classificationen
dc.subject.keywordjob advertisementsen
dc.titleAn Exploration of Representation Learning and Sequential Modeling Approaches for Supervised Topic Classification in Job Advertisementsen
dc.typeG2 Pro gradu, diplomityöfi
dc.type.okmG2 Pro gradu, diplomityö
dc.type.ontasotMaster's thesisen
dc.type.ontasotDiplomityöfi
dc.type.publicationmasterThesis
local.aalto.idinssi55007
local.aalto.openaccessyes
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
master_Westrup_Clemens_2016.pdf
Size:
2.18 MB
Format:
Adobe Portable Document Format