AMR dependency parsing with a typed semantic algebra
Jonas Groschwitz, Matthias Lindemann, Meaghan Fowlie, Mark Johnson, Alexander Koller
Abstract
We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree representations of the compositional structure of an AMR graph. This allows us to use standard neural techniques for supertagging and dependency tree parsing, constrained by a linguistically principled type system. We present two approximative decoding algorithms, which achieve state-of-the-art accuracy and outperform strong baselines.- Anthology ID:
- P18-1170
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1831–1841
- Language:
- URL:
- https://aclanthology.org/P18-1170
- DOI:
- 10.18653/v1/P18-1170
- Cite (ACL):
- Jonas Groschwitz, Matthias Lindemann, Meaghan Fowlie, Mark Johnson, and Alexander Koller. 2018. AMR dependency parsing with a typed semantic algebra. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1831–1841, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- AMR dependency parsing with a typed semantic algebra (Groschwitz et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/naacl24-info/P18-1170.pdf