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    <titleInfo>
        <title>Lyrics Segmentation: Textual Macrostructure Detection using Convolutions</title>
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    <name type="personal">
        <namePart type="given">Michael</namePart>
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    <name type="personal">
        <namePart type="given">Yaroslav</namePart>
        <namePart type="family">Nechaev</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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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">Fabien</namePart>
        <namePart type="family">Gandon</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Bender</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Leon</namePart>
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        <name type="personal">
            <namePart type="given">Pierre</namePart>
            <namePart type="family">Isabelle</namePart>
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    <abstract>Lyrics contain repeated patterns that are correlated with the repetitions found in the music they accompany. Repetitions in song texts have been shown to enable lyrics segmentation – a fundamental prerequisite of automatically detecting the building blocks (e.g. chorus, verse) of a song text. In this article we improve on the state-of-the-art in lyrics segmentation by applying a convolutional neural network to the task, and experiment with novel features as a step towards deeper macrostructure detection of lyrics.</abstract>
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        <date>2018-08</date>
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            <start>2044</start>
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%0 Conference Proceedings
%T Lyrics Segmentation: Textual Macrostructure Detection using Convolutions
%A Fell, Michael
%A Nechaev, Yaroslav
%A Cabrio, Elena
%A Gandon, Fabien
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F fell-etal-2018-lyrics
%X Lyrics contain repeated patterns that are correlated with the repetitions found in the music they accompany. Repetitions in song texts have been shown to enable lyrics segmentation – a fundamental prerequisite of automatically detecting the building blocks (e.g. chorus, verse) of a song text. In this article we improve on the state-of-the-art in lyrics segmentation by applying a convolutional neural network to the task, and experiment with novel features as a step towards deeper macrostructure detection of lyrics.
%U https://aclanthology.org/C18-1174
%P 2044-2054

Markdown (Informal)

[Lyrics Segmentation: Textual Macrostructure Detection using Convolutions](https://aclanthology.org/C18-1174) (Fell et al., COLING 2018)

• Lyrics Segmentation: Textual Macrostructure Detection using Convolutions (Fell et al., COLING 2018)

ACL

• Michael Fell, Yaroslav Nechaev, Elena Cabrio, and Fabien Gandon. 2018. Lyrics Segmentation: Textual Macrostructure Detection using Convolutions. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2044–2054, Santa Fe, New Mexico, USA. Association for Computational Linguistics.