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

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Mathioudakis, Michael
dc.contributor.author Westrup, Clemens
dc.date.accessioned 2016-11-02T09:46:44Z
dc.date.available 2016-11-02T09:46:44Z
dc.date.issued 2016-10-27
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/23375
dc.description.abstract This 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.extent 94+9
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.title An Exploration of Representation Learning and Sequential Modeling Approaches for Supervised Topic Classification in Job Advertisements en
dc.type G2 Pro gradu, diplomityö fi
dc.contributor.school Perustieteiden korkeakoulu fi
dc.subject.keyword natural language processing en
dc.subject.keyword computational linguistics en
dc.subject.keyword representation learning en
dc.subject.keyword sequential text modeling en
dc.subject.keyword text classification en
dc.subject.keyword job advertisements en
dc.identifier.urn URN:NBN:fi:aalto-201611025476
dc.programme.major Machine Learning and Data Mining fi
dc.programme.mcode SCI3015 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Gionis, Aristides
dc.programme Master’s Programme in Machine Learning and Data Mining (Macadamia) fi


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