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        <title>Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations</title>
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        <namePart type="given">Guangxiang</namePart>
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    <name type="personal">
        <namePart type="given">Xuancheng</namePart>
        <namePart type="family">Ren</namePart>
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    <abstract>This paper explores a new natural languageprocessing task, review-driven multi-label musicstyle classification. This task requires systemsto identify multiple styles of music basedon its reviews on websites. The biggest challengelies in the complicated relations of musicstyles. To tackle this problem, we proposea novel deep learning approach to automaticallylearn and exploit style correlations. Experiment results show that our approachachieves large improvements over baselines onthe proposed dataset. Furthermore, the visualizedanalysis shows that our approach performswell in capturing style correlations.</abstract>
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%0 Conference Proceedings
%T Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations
%A Zhao, Guangxiang
%A Xu, Jingjing
%A Zeng, Qi
%A Ren, Xuancheng
%A Sun, Xu
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F zhao-etal-2019-review
%X This paper explores a new natural languageprocessing task, review-driven multi-label musicstyle classification. This task requires systemsto identify multiple styles of music basedon its reviews on websites. The biggest challengelies in the complicated relations of musicstyles. To tackle this problem, we proposea novel deep learning approach to automaticallylearn and exploit style correlations. Experiment results show that our approachachieves large improvements over baselines onthe proposed dataset. Furthermore, the visualizedanalysis shows that our approach performswell in capturing style correlations.
%R 10.18653/v1/N19-1296
%U https://aclanthology.org/N19-1296
%U https://doi.org/10.18653/v1/N19-1296
%P 2884-2891

Markdown (Informal)

[Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations](https://aclanthology.org/N19-1296) (Zhao et al., NAACL 2019)

• Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations (Zhao et al., NAACL 2019)

ACL

• Guangxiang Zhao, Jingjing Xu, Qi Zeng, Xuancheng Ren, and Xu Sun. 2019. Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2884–2891, Minneapolis, Minnesota. Association for Computational Linguistics.