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        <title>Learning Representations for Detecting Abusive Language</title>
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    <name type="personal">
        <namePart type="given">Magnus</namePart>
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        <namePart type="given">Tim</namePart>
        <namePart type="family">Isbister</namePart>
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            <title>Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)</title>
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        <name type="personal">
            <namePart type="given">Darja</namePart>
            <namePart type="family">Fišer</namePart>
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        <name type="personal">
            <namePart type="given">Ruihong</namePart>
            <namePart type="family">Huang</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Vinodkumar</namePart>
            <namePart type="family">Prabhakaran</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Rob</namePart>
            <namePart type="family">Voigt</namePart>
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        <name type="personal">
            <namePart type="given">Zeerak</namePart>
            <namePart type="family">Waseem</namePart>
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        <name type="personal">
            <namePart type="given">Jacqueline</namePart>
            <namePart type="family">Wernimont</namePart>
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    <abstract>This paper discusses the question whether it is possible to learn a generic representation that is useful for detecting various types of abusive language. The approach is inspired by recent advances in transfer learning and word embeddings, and we learn representations from two different datasets containing various degrees of abusive language. We compare the learned representation with two standard approaches; one based on lexica, and one based on data-specific n-grams. Our experiments show that learned representations do contain useful information that can be used to improve detection performance when training data is limited.</abstract>
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    <identifier type="doi">10.18653/v1/W18-5115</identifier>
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%0 Conference Proceedings
%T Learning Representations for Detecting Abusive Language
%A Sahlgren, Magnus
%A Isbister, Tim
%A Olsson, Fredrik
%Y Fišer, Darja
%Y Huang, Ruihong
%Y Prabhakaran, Vinodkumar
%Y Voigt, Rob
%Y Waseem, Zeerak
%Y Wernimont, Jacqueline
%S Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F sahlgren-etal-2018-learning
%X This paper discusses the question whether it is possible to learn a generic representation that is useful for detecting various types of abusive language. The approach is inspired by recent advances in transfer learning and word embeddings, and we learn representations from two different datasets containing various degrees of abusive language. We compare the learned representation with two standard approaches; one based on lexica, and one based on data-specific n-grams. Our experiments show that learned representations do contain useful information that can be used to improve detection performance when training data is limited.
%R 10.18653/v1/W18-5115
%U https://aclanthology.org/W18-5115
%U https://doi.org/10.18653/v1/W18-5115
%P 115-123

Markdown (Informal)

[Learning Representations for Detecting Abusive Language](https://aclanthology.org/W18-5115) (Sahlgren et al., ALW 2018)

• Learning Representations for Detecting Abusive Language (Sahlgren et al., ALW 2018)

ACL

• Magnus Sahlgren, Tim Isbister, and Fredrik Olsson. 2018. Learning Representations for Detecting Abusive Language. In Proceedings of the 2nd Workshop on Abusive Language Online (ALW2), pages 115–123, Brussels, Belgium. Association for Computational Linguistics.