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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2020.semeval-1.276.pdf