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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-1528
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dc.contributor.authorvon Däniken, Pius-
dc.contributor.authorCieliebak, Mark-
dc.date.accessioned2017-12-14T14:19:12Z-
dc.date.available2017-12-14T14:19:12Z-
dc.date.issued2017-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/1854-
dc.description.abstractWe present our system for the WNUT 2017 Named Entity Recognition challenge on Twitter data. We describe two modifications of a basic neural network architecture for sequence tagging. First, we show how we exploit additional labeled data, where the Named Entity tags differ from the target task. Then, we propose a way to incorporate sentence level features. Our system uses both methods and ranked second for entity level annotations, achieving an F1-score of 40.78, and second for surface form annotations, achieving an F1-score of 39.33.de_CH
dc.language.isoende_CH
dc.publisherAssociation for Computational Linguisticsde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectNamed Entity Recogintionde_CH
dc.subjectNERde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleTransfer learning and sentence level features for named entity recognition on tweetsde_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-1528-
zhaw.conference.details3rd Workshop on Noisy User-generated Text (W-NUT), Copenhagen, Denmark, 7 September 2017de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end171de_CH
zhaw.pages.start166de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume3de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of the 3rd Workshop on Noisy User-generated Textde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedSoftware Systemsde_CH
zhaw.webfeedNatural Language Processingde_CH
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von Däniken, P., & Cieliebak, M. (2017). Transfer learning and sentence level features for named entity recognition on tweets [Conference paper]. Proceedings of the 3rd Workshop on Noisy User-Generated Text, 3, 166–171. https://doi.org/10.21256/zhaw-1528
von Däniken, P. and Cieliebak, M. (2017) ‘Transfer learning and sentence level features for named entity recognition on tweets’, in Proceedings of the 3rd Workshop on Noisy User-generated Text. Association for Computational Linguistics, pp. 166–171. Available at: https://doi.org/10.21256/zhaw-1528.
P. von Däniken and M. Cieliebak, “Transfer learning and sentence level features for named entity recognition on tweets,” in Proceedings of the 3rd Workshop on Noisy User-generated Text, 2017, vol. 3, pp. 166–171. doi: 10.21256/zhaw-1528.
VON DÄNIKEN, Pius und Mark CIELIEBAK, 2017. Transfer learning and sentence level features for named entity recognition on tweets. In: Proceedings of the 3rd Workshop on Noisy User-generated Text. Conference paper. Association for Computational Linguistics. 2017. S. 166–171
von Däniken, Pius, and Mark Cieliebak. 2017. “Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets.” Conference paper. In Proceedings of the 3rd Workshop on Noisy User-Generated Text, 3:166–71. Association for Computational Linguistics. https://doi.org/10.21256/zhaw-1528.
von Däniken, Pius, and Mark Cieliebak. “Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets.” Proceedings of the 3rd Workshop on Noisy User-Generated Text, vol. 3, Association for Computational Linguistics, 2017, pp. 166–71, https://doi.org/10.21256/zhaw-1528.


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