The Sally Smedley Hyperpartisan News Detector at SemEval-2019 Task 4

Kazuaki Hanawa, Shota Sasaki, Hiroki Ouchi, Jun Suzuki, Kentaro Inui


Abstract
This paper describes our system submitted to the formal run of SemEval-2019 Task 4: Hyperpartisan news detection. Our system is based on a linear classifier using several features, i.e., 1) embedding features based on the pre-trained BERT embeddings, 2) article length features, and 3) embedding features of informative phrases extracted from by-publisher dataset. Our system achieved 80.9% accuracy on the test set for the formal run and got the 3rd place out of 42 teams.
Anthology ID:
S19-2185
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1057–1061
Language:
URL:
https://aclanthology.org/S19-2185
DOI:
10.18653/v1/S19-2185
Bibkey:
Cite (ACL):
Kazuaki Hanawa, Shota Sasaki, Hiroki Ouchi, Jun Suzuki, and Kentaro Inui. 2019. The Sally Smedley Hyperpartisan News Detector at SemEval-2019 Task 4. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1057–1061, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
The Sally Smedley Hyperpartisan News Detector at SemEval-2019 Task 4 (Hanawa et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2185.pdf