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        <title>NAYEL at SemEval-2020 Task 12: TF/IDF-Based Approach for Automatic Offensive Language Detection in Arabic Tweets</title>
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        <namePart type="given">Hamada</namePart>
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            <namePart type="given">Aurelie</namePart>
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            <namePart type="given">Xiaodan</namePart>
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            <namePart type="given">Alexis</namePart>
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        <name type="personal">
            <namePart type="given">Nathan</namePart>
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            <namePart type="given">Jonathan</namePart>
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            <namePart type="given">Ekaterina</namePart>
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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
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%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)

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

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.