Abstract
This paper describes the Pioquinto Manterola Hyperpartisan News Detector, which participated in SemEval-2019 Task 4. Hyperpartisan news is highly polarized and takes a very biased or one–sided view of a particular story. We developed two variants of our system, the more successful was a Logistic Regression classifier based on unigram features. This was our official entry in the task, and it placed 23rd of 42 participating teams. Our second variant was a Convolutional Neural Network that did not perform as well.- Anthology ID:
- S19-2162
- 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:
- 949–953
- Language:
- URL:
- https://aclanthology.org/S19-2162
- DOI:
- 10.18653/v1/S19-2162
- Cite (ACL):
- Saptarshi Sengupta and Ted Pedersen. 2019. Duluth at SemEval-2019 Task 4: The Pioquinto Manterola Hyperpartisan News Detector. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 949–953, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- Duluth at SemEval-2019 Task 4: The Pioquinto Manterola Hyperpartisan News Detector (Sengupta & Pedersen, SemEval 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/S19-2162.pdf