Naslov
TakeLab at SemEval-2019 Task 4: Hyperpartisan News Detection

Autori
Palić, Niko ; Vladika, Juraj ; Čubelić, Dominik ; Lovrenčić, Ivan ; Buljan, Maja ; Šnajder, Jan

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019) / - : Association for Computational Linguistics (ACL), 2019, 995-998

Skup
The 13th International Workshop on Semantic Evaluation (SemEval-2019)

Mjesto i datum
Minneapolis (MN), Sjedinjene Američke Države, 06.06.2019. - 07.06.2019

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Hyperpartisan News Detection ; machine learning

Sažetak
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%).

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA