Cardiff University at SemEval-2019 Task 4: Linguistic Features for Hyperpartisan News Detection

Carla Pérez-Almendros, Luis Espinosa-Anke, Steven Schockaert


Abstract
This paper summarizes our contribution to the Hyperpartisan News Detection task in SemEval 2019. We experiment with two different approaches: 1) an SVM classifier based on word vector averages and hand-crafted linguistic features, and 2) a BiLSTM-based neural text classifier trained on a filtered training set. Surprisingly, despite their different nature, both approaches achieve an accuracy of 0.74. The main focus of this paper is to further analyze the remarkable fact that a simple feature-based approach can perform on par with modern neural classifiers. We also highlight the effectiveness of our filtering strategy for training the neural network on a large but noisy training set.
Anthology ID:
S19-2158
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:
929–933
Language:
URL:
https://aclanthology.org/S19-2158
DOI:
10.18653/v1/S19-2158
Bibkey:
Cite (ACL):
Carla Pérez-Almendros, Luis Espinosa-Anke, and Steven Schockaert. 2019. Cardiff University at SemEval-2019 Task 4: Linguistic Features for Hyperpartisan News Detection. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 929–933, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
Cardiff University at SemEval-2019 Task 4: Linguistic Features for Hyperpartisan News Detection (Pérez-Almendros et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2158.pdf