Abstract
In this paper, we describe our attempt to learn bias from news articles. From our experiments, it seems that although there is a correlation between publisher bias and article bias, it is challenging to learn bias directly from the publisher labels. On the other hand, using few manually-labeled samples can increase the accuracy metric from around 60% to near 80%. Our system is computationally inexpensive and uses several standard document representations in NLP to train an SVM or LR classifier. The system ranked 4th in the SemEval-2019 task. The code is released for reproducibility.- Anthology ID:
- S19-2187
- 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:
- 1067–1071
- Language:
- URL:
- https://aclanthology.org/S19-2187
- DOI:
- 10.18653/v1/S19-2187
- Cite (ACL):
- Chia-Lun Yeh, Babak Loni, and Anne Schuth. 2019. Tom Jumbo-Grumbo at SemEval-2019 Task 4: Hyperpartisan News Detection with GloVe vectors and SVM. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1067–1071, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- Tom Jumbo-Grumbo at SemEval-2019 Task 4: Hyperpartisan News Detection with GloVe vectors and SVM (Yeh et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/S19-2187.pdf