Brenda Starr at SemEval-2019 Task 4: Hyperpartisan News Detection

Olga Papadopoulou, Giorgos Kordopatis-Zilos, Markos Zampoglou, Symeon Papadopoulos, Yiannis Kompatsiaris


Abstract
In the effort to tackle the challenge of Hyperpartisan News Detection, i.e., the task of deciding whether a news article is biased towards one party, faction, cause, or person, we experimented with two systems: i) a standard supervised learning approach using superficial text and bag-of-words features from the article title and body, and ii) a deep learning system comprising a four-layer convolutional neural network and max-pooling layers after the embedding layer, feeding the consolidated features to a bi-directional recurrent neural network. We achieved an F-score of 0.712 with our best approach, which corresponds to the mid-range of performance levels in the leaderboard.
Anthology ID:
S19-2157
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:
924–928
Language:
URL:
https://aclanthology.org/S19-2157
DOI:
10.18653/v1/S19-2157
Bibkey:
Cite (ACL):
Olga Papadopoulou, Giorgos Kordopatis-Zilos, Markos Zampoglou, Symeon Papadopoulos, and Yiannis Kompatsiaris. 2019. Brenda Starr at SemEval-2019 Task 4: Hyperpartisan News Detection. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 924–928, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
Brenda Starr at SemEval-2019 Task 4: Hyperpartisan News Detection (Papadopoulou et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2157.pdf