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dc.contributor.authorDeng, Qiao
dc.date.accessioned2018-08-22T22:00:18Z
dc.date.issued2018
dc.identifier.citationDeng, Qiao. Automatic Evaluation of Topic Modeling. Master thesis, University of Oslo, 2018
dc.identifier.urihttp://hdl.handle.net/10852/63613
dc.description.abstractIn this thesis, we aim to find suitable automatic evaluation metrics for evaluating topic models in the Aftenposten news domain. We review and experiment with six evaluation metrics from previous research which are potentially corresponding with human judgments. Most importantly, we carry out two secondary tasks with the metadata we have, to evaluate the performance of these evaluation metrics. From these tasks we find that given the same number of topics, inter-doc similarity and within-doc rank metrics are the most suitable metrics for evaluating topic models which are trained to rank relevant documents, and that inter-doc similarity is also suitable for evaluating models which are trained to classify documents into sections in the Aftenposten corpus. These metrics can be directly adopted to evaluate topic models which are trained for related use cases in the future.eng
dc.language.isoeng
dc.subjecttopic modeling
dc.subjectevaluation of topic models
dc.titleAutomatic Evaluation of Topic Modelingeng
dc.typeMaster thesis
dc.date.updated2018-08-22T22:00:18Z
dc.creator.authorDeng, Qiao
dc.date.embargoenddate3018-05-16
dc.rights.termsKLAUSULERING: Dokumentet er klausulert grunnet lovpålagt taushetsplikt. Tilgangskode/Access code C
dc.identifier.urnURN:NBN:no-66152
dc.type.documentMasteroppgave
dc.rights.accessrightsclosedaccess
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/63613/1/thesis.pdf


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