Abstract
This paper describes our submission to task 4 in SemEval 2019, i.e., hyperpartisan news detection. Our model aims at detecting hyperpartisan news by incorporating the style-based features and the content-based features. We extract a broad number of feature sets and use as our learning algorithms the GBDT and the n-gram CNN model. Finally, we apply the weighted average for effective learning between the two models. Our model achieves an accuracy of 0.745 on the test set in subtask A.- Anthology ID:
- S19-2171
- 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:
- 990–994
- Language:
- URL:
- https://aclanthology.org/S19-2171
- DOI:
- 10.18653/v1/S19-2171
- Cite (ACL):
- Youngjun Joo and Inchon Hwang. 2019. Steve Martin at SemEval-2019 Task 4: Ensemble Learning Model for Detecting Hyperpartisan News. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 990–994, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- Steve Martin at SemEval-2019 Task 4: Ensemble Learning Model for Detecting Hyperpartisan News (Joo & Hwang, SemEval 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/S19-2171.pdf