NAYEL at SemEval-2020 Task 12: TF/IDF-Based Approach for Automatic Offensive Language Detection in Arabic Tweets

Hamada Nayel


Abstract
In this paper, we present the system submitted to “SemEval-2020 Task 12”. The proposed system aims at automatically identify the Offensive Language in Arabic Tweets. A machine learning based approach has been used to design our system. We implemented a linear classifier with Stochastic Gradient Descent (SGD) as optimization algorithm. Our model reported 84.20%, 81.82% f1-score on development set and test set respectively. The best performed system and the system in the last rank reported 90.17% and 44.51% f1-score on test set respectively.
Anthology ID:
2020.semeval-1.276
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
2086–2089
Language:
URL:
https://aclanthology.org/2020.semeval-1.276
DOI:
10.18653/v1/2020.semeval-1.276
Bibkey:
Cite (ACL):
Hamada Nayel. 2020. NAYEL at SemEval-2020 Task 12: TF/IDF-Based Approach for Automatic Offensive Language Detection in Arabic Tweets. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 2086–2089, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
NAYEL at SemEval-2020 Task 12: TF/IDF-Based Approach for Automatic Offensive Language Detection in Arabic Tweets (Nayel, SemEval 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.276.pdf