Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations
Guangxiang Zhao, Jingjing Xu, Qi Zeng, Xuancheng Ren, Xu Sun
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.- Anthology ID:
- N19-1296
- Volume:
- 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)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Jill Burstein, Christy Doran, Thamar Solorio
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2884–2891
- Language:
- URL:
- https://aclanthology.org/N19-1296
- DOI:
- 10.18653/v1/N19-1296
- Cite (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.
- Cite (Informal):
- Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations (Zhao et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/N19-1296.pdf
- Code
- lancopku/RMSC