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        <title>Garain at SemEval-2020 Task 12: Sequence Based Deep Learning for Categorizing Offensive Language in Social Media</title>
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    <name type="personal">
        <namePart type="given">Avishek</namePart>
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            <title>Proceedings of the Fourteenth Workshop on Semantic Evaluation</title>
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            <namePart type="given">Aurelie</namePart>
            <namePart type="family">Herbelot</namePart>
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            <namePart type="given">Xiaodan</namePart>
            <namePart type="family">Zhu</namePart>
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        <name type="personal">
            <namePart type="given">Nathan</namePart>
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        <name type="personal">
            <namePart type="given">Jonathan</namePart>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Shutova</namePart>
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    <abstract>SemEval-2020 Task 12 was OffenseEval: Multilingual Offensive Language Identification inSocial Media (Zampieri et al., 2020). The task was subdivided into multiple languages anddatasets were provided for each one. The task was further divided into three sub-tasks: offensivelanguage identification, automatic categorization of offense types, and offense target identification.I participated in the task-C, that is, offense target identification. For preparing the proposed system,I made use of Deep Learning networks like LSTMs and frameworks like Keras which combine thebag of words model with automatically generated sequence based features and manually extractedfeatures from the given dataset. My system on training on 25% of the whole dataset achieves macro averaged f1 score of 47.763%.</abstract>
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    <identifier type="doi">10.18653/v1/2020.semeval-1.257</identifier>
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            <start>1953</start>
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%0 Conference Proceedings
%T Garain at SemEval-2020 Task 12: Sequence Based Deep Learning for Categorizing Offensive Language in Social Media
%A Garain, Avishek
%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 garain-2020-garain
%X SemEval-2020 Task 12 was OffenseEval: Multilingual Offensive Language Identification inSocial Media (Zampieri et al., 2020). The task was subdivided into multiple languages anddatasets were provided for each one. The task was further divided into three sub-tasks: offensivelanguage identification, automatic categorization of offense types, and offense target identification.I participated in the task-C, that is, offense target identification. For preparing the proposed system,I made use of Deep Learning networks like LSTMs and frameworks like Keras which combine thebag of words model with automatically generated sequence based features and manually extractedfeatures from the given dataset. My system on training on 25% of the whole dataset achieves macro averaged f1 score of 47.763%.
%R 10.18653/v1/2020.semeval-1.257
%U https://aclanthology.org/2020.semeval-1.257/
%U https://doi.org/10.18653/v1/2020.semeval-1.257
%P 1953-1960

Markdown (Informal)

[Garain at SemEval-2020 Task 12: Sequence Based Deep Learning for Categorizing Offensive Language in Social Media](https://aclanthology.org/2020.semeval-1.257/) (Garain, SemEval 2020)

• Garain at SemEval-2020 Task 12: Sequence Based Deep Learning for Categorizing Offensive Language in Social Media (Garain, SemEval 2020)

ACL

• Avishek Garain. 2020. Garain at SemEval-2020 Task 12: Sequence Based Deep Learning for Categorizing Offensive Language in Social Media. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1953–1960, Barcelona (online). International Committee for Computational Linguistics.