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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/S19-2157.pdf