Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-1531
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dc.contributor.authorKodiyan, Don-
dc.contributor.authorHardegger, Florin-
dc.contributor.authorNeuhaus, Stephan-
dc.contributor.authorCieliebak, Mark-
dc.date.accessioned2017-12-15T07:54:20Z-
dc.date.available2017-12-15T07:54:20Z-
dc.date.issued2017-
dc.identifier.issn1613-0073de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/1865-
dc.description.abstractThis paper describes our approach for the Author Profiling Shared Task at PAN 2017. The goal was to classify the gender and language variety of a Twitter user solely by their tweets. Author Profiling can be applied in various fields like marketing, security and forensics. Twitter already uses similar techniques to deliver personalized advertisement for their users. PAN 2017 provided a corpus for this purpose in the languages: English, Spanish, Portuguese and Arabic. To solve the problem we used a deep learning approach, which has shown recent success in Natural Language Processing. Our submitted model consists of a bidirectional Recurrent Neural Network implemented with a Gated Recurrent Unit (GRU) combined with an Attention Mechanism. We achieved an average accuracy over all languages of 75,31% in gender classification and 85,22% in language variety classification.de_CH
dc.language.isoende_CH
dc.publisherRWTH Aachende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectGender Classificationde_CH
dc.subjectAuthor Profilingde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleAuthor profiling with bidirectional RNNs using attention with GRUs : notebook for PAN at CLEF 2017de_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.21256/zhaw-1531-
zhaw.conference.detailsCLEF 2017 Conference and Labs of the Evaluation Forum, Dublin, Ireland, 11-14 September 2017de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume1866de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsCLEF 2017 Evaluation Labs and Workshop – Working Notes Papersde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedSoftware Systemsde_CH
zhaw.webfeedNatural Language Processingde_CH
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Kodiyan, D., Hardegger, F., Neuhaus, S., & Cieliebak, M. (2017). Author profiling with bidirectional RNNs using attention with GRUs : notebook for PAN at CLEF 2017 [Conference paper]. CLEF 2017 Evaluation Labs and Workshop – Working Notes Papers, 1866. https://doi.org/10.21256/zhaw-1531
Kodiyan, D. et al. (2017) ‘Author profiling with bidirectional RNNs using attention with GRUs : notebook for PAN at CLEF 2017’, in CLEF 2017 Evaluation Labs and Workshop – Working Notes Papers. RWTH Aachen. Available at: https://doi.org/10.21256/zhaw-1531.
D. Kodiyan, F. Hardegger, S. Neuhaus, and M. Cieliebak, “Author profiling with bidirectional RNNs using attention with GRUs : notebook for PAN at CLEF 2017,” in CLEF 2017 Evaluation Labs and Workshop – Working Notes Papers, 2017, vol. 1866. doi: 10.21256/zhaw-1531.
KODIYAN, Don, Florin HARDEGGER, Stephan NEUHAUS und Mark CIELIEBAK, 2017. Author profiling with bidirectional RNNs using attention with GRUs : notebook for PAN at CLEF 2017. In: CLEF 2017 Evaluation Labs and Workshop – Working Notes Papers. Conference paper. RWTH Aachen. 2017
Kodiyan, Don, Florin Hardegger, Stephan Neuhaus, and Mark Cieliebak. 2017. “Author Profiling with Bidirectional RNNs Using Attention with GRUs : Notebook for PAN at CLEF 2017.” Conference paper. In CLEF 2017 Evaluation Labs and Workshop – Working Notes Papers. Vol. 1866. RWTH Aachen. https://doi.org/10.21256/zhaw-1531.
Kodiyan, Don, et al. “Author Profiling with Bidirectional RNNs Using Attention with GRUs : Notebook for PAN at CLEF 2017.” CLEF 2017 Evaluation Labs and Workshop – Working Notes Papers, vol. 1866, RWTH Aachen, 2017, https://doi.org/10.21256/zhaw-1531.


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