NLP@UIT at SemEval-2019 Task 4: The Paparazzo Hyperpartisan News Detector

Duc-Vu Nguyen, Thin Dang, Ngan Nguyen


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
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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/S19-2167.pdf