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.- Anthology ID:
- C18-1174
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Editors:
- Emily M. Bender, Leon Derczynski, Pierre Isabelle
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2044–2054
- Language:
- URL:
- https://aclanthology.org/C18-1174
- DOI:
- Cite (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.
- Cite (Informal):
- Lyrics Segmentation: Textual Macrostructure Detection using Convolutions (Fell et al., COLING 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/C18-1174.pdf
- Code
- TuringTrain/lyrics_segmentation