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    <titleInfo>
        <title>Comparing Automated Methods to Detect Explicit Content in Song Lyrics</title>
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    <name type="personal">
        <namePart type="given">Michael</namePart>
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    <name type="personal">
        <namePart type="given">Elena</namePart>
        <namePart type="family">Cabrio</namePart>
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    <name type="personal">
        <namePart type="given">Michele</namePart>
        <namePart type="family">Corazza</namePart>
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    <name type="personal">
        <namePart type="given">Fabien</namePart>
        <namePart type="family">Gandon</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>The Parental Advisory Label (PAL) is a warning label that is placed on audio recordings in recognition of profanity or inappropriate references, with the intention of alerting parents of material potentially unsuitable for children. Since 2015, digital providers – such as iTunes, Spotify, Amazon Music and Deezer – also follow PAL guidelines and tag such tracks as “explicit”. Nowadays, such labelling is carried out mainly manually on voluntary basis, with the drawbacks of being time consuming and therefore costly, error prone and partly a subjective task. In this paper, we compare automated methods ranging from dictionary-based lookup to state-of-the-art deep neural networks to automatically detect explicit contents in English lyrics. We show that more complex models perform only slightly better on this task, and relying on a qualitative analysis of the data, we discuss the inherent hardness and subjectivity of the task.</abstract>
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    <identifier type="doi">10.26615/978-954-452-056-4_039</identifier>
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        <date>2019-09</date>
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%0 Conference Proceedings
%T Comparing Automated Methods to Detect Explicit Content in Song Lyrics
%A Fell, Michael
%A Cabrio, Elena
%A Corazza, Michele
%A Gandon, Fabien
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F fell-etal-2019-comparing
%X The Parental Advisory Label (PAL) is a warning label that is placed on audio recordings in recognition of profanity or inappropriate references, with the intention of alerting parents of material potentially unsuitable for children. Since 2015, digital providers – such as iTunes, Spotify, Amazon Music and Deezer – also follow PAL guidelines and tag such tracks as “explicit”. Nowadays, such labelling is carried out mainly manually on voluntary basis, with the drawbacks of being time consuming and therefore costly, error prone and partly a subjective task. In this paper, we compare automated methods ranging from dictionary-based lookup to state-of-the-art deep neural networks to automatically detect explicit contents in English lyrics. We show that more complex models perform only slightly better on this task, and relying on a qualitative analysis of the data, we discuss the inherent hardness and subjectivity of the task.
%R 10.26615/978-954-452-056-4_039
%U https://aclanthology.org/R19-1039
%U https://doi.org/10.26615/978-954-452-056-4_039
%P 338-344

Markdown (Informal)

[Comparing Automated Methods to Detect Explicit Content in Song Lyrics](https://aclanthology.org/R19-1039) (Fell et al., RANLP 2019)

• Comparing Automated Methods to Detect Explicit Content in Song Lyrics (Fell et al., RANLP 2019)

ACL

• Michael Fell, Elena Cabrio, Michele Corazza, and Fabien Gandon. 2019. Comparing Automated Methods to Detect Explicit Content in Song Lyrics. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 338–344, Varna, Bulgaria. INCOMA Ltd..