@inproceedings{nayel-2020-nayel,
title = "{NAYEL} at {S}em{E}val-2020 Task 12: {TF}/{IDF}-Based Approach for Automatic Offensive Language Detection in {A}rabic Tweets",
author = "Nayel, Hamada",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.276",
doi = "10.18653/v1/2020.semeval-1.276",
pages = "2086--2089",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T NAYEL at SemEval-2020 Task 12: TF/IDF-Based Approach for Automatic Offensive Language Detection in Arabic Tweets
%A Nayel, Hamada
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 dec
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F nayel-2020-nayel
%X 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.
%R 10.18653/v1/2020.semeval-1.276
%U https://aclanthology.org/2020.semeval-1.276
%U https://doi.org/10.18653/v1/2020.semeval-1.276
%P 2086-2089
Markdown (Informal)
[NAYEL at SemEval-2020 Task 12: TF/IDF-Based Approach for Automatic Offensive Language Detection in Arabic Tweets](https://aclanthology.org/2020.semeval-1.276) (Nayel, SemEval 2020)
ACL