| publication name | NAYEL at SemEval-2020 Task 12: TF/IDF-Based Approach for Automatic Offensive Language Detection in Arabic Tweets |
|---|---|
| Authors | Hamada A. Nayel |
| year | 2020 |
| keywords | Offensive language detection; Arabic NLP, Social Media Analysis |
| journal | Proceedings of the Fourteenth Workshop on Semantic Evaluation |
| volume | 2020 |
| issue | Not Available |
| pages | 2086–2089 |
| publisher | International Committee for Computational Linguistics |
| Local/International | International |
| Paper Link | https://www.aclweb.org/anthology/2020.semeval-1.276 |
| Full paper | download |
| Supplementary materials | Not Available |
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.