Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks
Albin Zehe, Lena Hettinger, Stefan Ernst, Christian Hauptmann, Andreas Hotho
Abstract
This paper describes our system for the SemEval 2019 Task 4 on hyperpartisan news detection. We build on an existing deep learning approach for sentence classification based on a Convolutional Neural Network. Modifying the original model with additional layers to increase its expressiveness and finally building an ensemble of multiple versions of the model, we obtain an accuracy of 67.52% and an F1 score of 73.78% on the main test dataset. We also report on additional experiments incorporating handcrafted features into the CNN and using it as a feature extractor for a linear SVM.- Anthology ID:
- S19-2183
- 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:
- 1047–1051
- Language:
- URL:
- https://aclanthology.org/S19-2183
- DOI:
- 10.18653/v1/S19-2183
- Cite (ACL):
- Albin Zehe, Lena Hettinger, Stefan Ernst, Christian Hauptmann, and Andreas Hotho. 2019. Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1047–1051, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks (Zehe et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/S19-2183.pdf