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    <titleInfo>
        <title>Contextual-Lexicon Approach for Abusive Language Detection</title>
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    <name type="personal">
        <namePart type="given">Francielle</namePart>
        <namePart type="family">Vargas</namePart>
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        <namePart type="given">Fabiana</namePart>
        <namePart type="family">Rodrigues de Góes</namePart>
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    <name type="personal">
        <namePart type="given">Isabelle</namePart>
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        <namePart type="given">Fabrício</namePart>
        <namePart type="family">Benevenuto</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>Since a lexicon-based approach is more elegant scientifically, explaining the solution components and being easier to generalize to other applications, this paper provides a new approach for offensive language and hate speech detection on social media, which embodies a lexicon of implicit and explicit offensive and swearing expressions annotated with contextual information. Due to the severity of the social media abusive comments in Brazil, and the lack of research in Portuguese, Brazilian Portuguese is the language used to validate the models. Nevertheless, our method may be applied to any other language. The conducted experiments show the effectiveness of the proposed approach, outperforming the current baseline methods for the Portuguese language.</abstract>
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        <date>2021-09</date>
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%0 Conference Proceedings
%T Contextual-Lexicon Approach for Abusive Language Detection
%A Vargas, Francielle
%A Rodrigues de Góes, Fabiana
%A Carvalho, Isabelle
%A Benevenuto, Fabrício
%A Pardo, Thiago
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F vargas-etal-2021-contextual
%X Since a lexicon-based approach is more elegant scientifically, explaining the solution components and being easier to generalize to other applications, this paper provides a new approach for offensive language and hate speech detection on social media, which embodies a lexicon of implicit and explicit offensive and swearing expressions annotated with contextual information. Due to the severity of the social media abusive comments in Brazil, and the lack of research in Portuguese, Brazilian Portuguese is the language used to validate the models. Nevertheless, our method may be applied to any other language. The conducted experiments show the effectiveness of the proposed approach, outperforming the current baseline methods for the Portuguese language.
%U https://aclanthology.org/2021.ranlp-1.161/
%P 1438-1447

Markdown (Informal)

[Contextual-Lexicon Approach for Abusive Language Detection](https://aclanthology.org/2021.ranlp-1.161/) (Vargas et al., RANLP 2021)

• Contextual-Lexicon Approach for Abusive Language Detection (Vargas et al., RANLP 2021)

ACL

• Francielle Vargas, Fabiana Rodrigues de Góes, Isabelle Carvalho, Fabrício Benevenuto, and Thiago Pardo. 2021. Contextual-Lexicon Approach for Abusive Language Detection. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1438–1447, Held Online. INCOMA Ltd..