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  2. School of Engineering
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dc.contributor.authorUzdilli, Fatih-
dc.contributor.authorJaggi, Martin-
dc.contributor.authorEgger, Dominic-
dc.contributor.authorJulmy, Pascal-
dc.contributor.authorDerczynski, Leon-
dc.contributor.authorCieliebak, Mark-
dc.date.accessioned2017-12-15T14:54:17Z-
dc.date.available2017-12-15T14:54:17Z-
dc.date.issued2015-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/1881-
dc.description.abstractWe describe a classifier for predicting message-level sentiment of English microblog messages from Twitter. This paper describes our submission to the SemEval-2015 competition (Task 10). Our approach is to combine several variants of our previous year’s SVM system into one meta-classifier, which was then trained using a random forest. The main idea is that the meta-classifier allows the combination of the strengths and overcome some of the weaknesses of the artificially-built individual classifiers, and adds additional non-linearity. We were also able to improve the linear classifiers by using a new regularization technique we call flipout.de_CH
dc.language.isoende_CH
dc.publisherAssociation for Computational Linguisticsde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc004: Informatikde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.titleSwiss-Chocolate : combining flipout regularization and random forests with artificially built subsystems to boost text-classification for sentimentde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.18653/v1/S15-2101de_CH
zhaw.conference.detailsInternational Workshop on Semantic Evaluation (SemEval-2014), Dublin, Irland, 23-24 August 2014de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end612de_CH
zhaw.pages.start608de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume9de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, June 4-5, 2015de_CH
zhaw.webfeedSoftware Systemsde_CH
zhaw.webfeedNatural Language Processingde_CH
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Uzdilli, F., Jaggi, M., Egger, D., Julmy, P., Derczynski, L., & Cieliebak, M. (2015). Swiss-Chocolate : combining flipout regularization and random forests with artificially built subsystems to boost text-classification for sentiment [Conference paper]. Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, June 4-5, 2015, 9, 608–612. https://doi.org/10.18653/v1/S15-2101
Uzdilli, F. et al. (2015) ‘Swiss-Chocolate : combining flipout regularization and random forests with artificially built subsystems to boost text-classification for sentiment’, in Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, June 4-5, 2015. Association for Computational Linguistics, pp. 608–612. Available at: https://doi.org/10.18653/v1/S15-2101.
F. Uzdilli, M. Jaggi, D. Egger, P. Julmy, L. Derczynski, and M. Cieliebak, “Swiss-Chocolate : combining flipout regularization and random forests with artificially built subsystems to boost text-classification for sentiment,” in Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, June 4-5, 2015, 2015, vol. 9, pp. 608–612. doi: 10.18653/v1/S15-2101.
UZDILLI, Fatih, Martin JAGGI, Dominic EGGER, Pascal JULMY, Leon DERCZYNSKI und Mark CIELIEBAK, 2015. Swiss-Chocolate : combining flipout regularization and random forests with artificially built subsystems to boost text-classification for sentiment. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, June 4-5, 2015. Conference paper. Association for Computational Linguistics. 2015. S. 608–612
Uzdilli, Fatih, Martin Jaggi, Dominic Egger, Pascal Julmy, Leon Derczynski, and Mark Cieliebak. 2015. “Swiss-Chocolate : Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment.” Conference paper. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, June 4-5, 2015, 9:608–12. Association for Computational Linguistics. https://doi.org/10.18653/v1/S15-2101.
Uzdilli, Fatih, et al. “Swiss-Chocolate : Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment.” Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, June 4-5, 2015, vol. 9, Association for Computational Linguistics, 2015, pp. 608–12, https://doi.org/10.18653/v1/S15-2101.


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