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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/S19-2158.pdf