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://preview.aclanthology.org/build-pipeline-with-new-library/S19-2185/
- DOI:
- 10.18653/v1/S19-2185
- 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)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/S19-2185.pdf