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dc.contributor.authorHammer, Hugo Lewi
dc.contributor.authorYazidi, Anis
dc.contributor.authorBai, Aleksander
dc.contributor.authorEngelstad, Paal E.
dc.date.accessioned2017-02-03T12:16:36Z
dc.date.accessioned2017-02-14T10:09:44Z
dc.date.available2017-02-03T12:16:36Z
dc.date.available2017-02-14T10:09:44Z
dc.date.issued2016
dc.identifier.citationHammer HL, Yazidi A, Bai A, Engelstad P.E.: Improving classification of tweets using word-word co-occurrence information from a large external corpus. In: Ossowski S. Proceedings of the 31st Annual ACM Symposium on Applied Computing (SAC '16), 2016. Association for Computing Machinery (ACM) p. 1174-1177language
dc.identifier.urihttps://hdl.handle.net/10642/3723
dc.description.abstractClassifying tweets is an intrinsically hard task as tweets are short messages which makes traditional bags of words based approach ine cient. In fact, bags of words approaches ig- nores relationships between important terms that do not co-occur literally. In this paper we resort to word-word co-occurence informa- tion from a large corpus to expand the vocabulary of another corpus consisting of tweets. Our results show that we are able to reduce the number of erroneous classi cations by 14% using co-occurence information.language
dc.language.isoenlanguage
dc.publisherAssociation for Computing Machinery (ACM)language
dc.subjectClassificationlanguage
dc.subjectLasso regressionlanguage
dc.subjectTwitterlanguage
dc.subjectWord-word co-occurencelanguage
dc.titleImproving classification of tweets using word-word co-occurrence information from a large external corpuslanguage
dc.title.alternativeProceedings of the 31st Annual ACM Symposium on Applied Computing (SAC '16)language
dc.typeChapter
dc.typePeer reviewedlanguage
dc.typeChapter
dc.date.updated2017-02-03T12:16:36Z
dc.description.versionacceptedVersionlanguage
dc.identifier.doihttp://dx.doi.org/10.1145/2851613.2851986
dc.identifier.cristin1383941
dc.source.isbn978-1-4503-3739-7


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