Abstract
Various linearizations have been proposed to cast syntactic dependency parsing as sequence labeling. However, these approaches do not support more complex graph-based representations, such as semantic dependencies or enhanced universal dependencies, as they cannot handle reentrancy or cycles. By extending them, we define a range of unbounded and bounded linearizations that can be used to cast graph parsing as a tagging task, enlarging the toolbox of problems that can be solved under this paradigm. Experimental results on semantic dependency and enhanced UD parsing show that with a good choice of encoding, sequence-labeling semantic dependency parsers combine high efficiency with accuracies close to the state of the art, in spite of their simplicity.- Anthology ID:
- 2024.emnlp-main.659
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 11804–11828
- Language:
- URL:
- https://aclanthology.org/2024.emnlp-main.659
- DOI:
- 10.18653/v1/2024.emnlp-main.659
- Cite (ACL):
- Ana Ezquerro, David Vilares, and Carlos Gómez-Rodríguez. 2024. Dependency Graph Parsing as Sequence Labeling. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 11804–11828, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Dependency Graph Parsing as Sequence Labeling (Ezquerro et al., EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.emnlp-main.659.pdf