@inproceedings{palic-etal-2019-takelab,
title = "{T}ake{L}ab at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection",
author = "Pali{\'c}, Niko and
Vladika, Juraj and
{\v{C}}ubeli{\'c}, Dominik and
Lovren{\v{c}}i{\'c}, Ivan and
Buljan, Maja and
{\v{S}}najder, Jan",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2172",
doi = "10.18653/v1/S19-2172",
pages = "995--998",
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{\%}).",
}
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%0 Conference Proceedings
%T TakeLab at SemEval-2019 Task 4: Hyperpartisan News Detection
%A Palić, Niko
%A Vladika, Juraj
%A Čubelić, Dominik
%A Lovrenčić, Ivan
%A Buljan, Maja
%A Šnajder, Jan
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F palic-etal-2019-takelab
%X 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%).
%R 10.18653/v1/S19-2172
%U https://aclanthology.org/S19-2172
%U https://doi.org/10.18653/v1/S19-2172
%P 995-998
Markdown (Informal)
[TakeLab at SemEval-2019 Task 4: Hyperpartisan News Detection](https://aclanthology.org/S19-2172) (Palić et al., SemEval 2019)
ACL
- Niko Palić, Juraj Vladika, Dominik Čubelić, Ivan Lovrenčić, Maja Buljan, and Jan Šnajder. 2019. TakeLab at SemEval-2019 Task 4: Hyperpartisan News Detection. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 995–998, Minneapolis, Minnesota, USA. Association for Computational Linguistics.