TakeLab at SemEval-2019 task 4: Hyperpartisan news detection

borat-sagdiyev team

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

5 Zitate (Scopus)

Abstract

In this paper, we demonstrate the system built to solve the SemEval-2019 task 4: Hyperpartisan News Detection (Kiesel et al., 2019), the task of automatically determining whether an article is heavily biased towards one side of the political spectrum. Our system receives an article in its raw, textual form, analyzes it, and predicts with moderate accuracy whether the article is hyperpartisan. The learning model used was primarily trained on a manually prelabeled dataset containing news articles. The system relies on the previously constructed SVM model, available in the Python Scikit-Learn library. We ranked 6th in the competition of 42 teams with an accuracy of 79.1% (the winning team had 82.2%).

OriginalspracheEnglisch
TitelNAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop
Herausgeber (Verlag)Association for Computational Linguistics (ACL)
Seiten995-998
Seitenumfang4
ISBN (elektronisch)9781950737062
PublikationsstatusVeröffentlicht - 2019
Extern publiziertJa
Veranstaltung13th International Workshop on Semantic Evaluation, SemEval 2019, co-located with the 17th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019 - Minneapolis, USA/Vereinigte Staaten
Dauer: 6 Juni 20197 Juni 2019

Publikationsreihe

NameNAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop

Konferenz

Konferenz13th International Workshop on Semantic Evaluation, SemEval 2019, co-located with the 17th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019
Land/GebietUSA/Vereinigte Staaten
OrtMinneapolis
Zeitraum6/06/197/06/19

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