Abstract
This paper describes the system of NLP@UIT that participated in Task 4 of SemEval-2019. We developed a system that predicts whether an English news article follows a hyperpartisan argumentation. Paparazzo is the name of our system and is also the code name of our team in Task 4 of SemEval-2019. The Paparazzo system, in which we use tri-grams of words and hepta-grams of characters, officially ranks thirteen with an accuracy of 0.747. Another system of ours, which utilizes trigrams of words, tri-grams of characters, trigrams of part-of-speech, syntactic dependency sub-trees, and named-entity recognition tags, achieved an accuracy of 0.787 and is proposed after the deadline of Task 4.- Anthology ID:
- S19-2167
- 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:
- 971–975
- Language:
- URL:
- https://aclanthology.org/S19-2167
- DOI:
- 10.18653/v1/S19-2167
- Cite (ACL):
- Duc-Vu Nguyen, Thin Dang, and Ngan Nguyen. 2019. NLP@UIT at SemEval-2019 Task 4: The Paparazzo Hyperpartisan News Detector. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 971–975, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- NLP@UIT at SemEval-2019 Task 4: The Paparazzo Hyperpartisan News Detector (Nguyen et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/S19-2167.pdf