Abstract
Symbolic meaning representations of natural language text have been studied since at least the 1960s. With the availability of large annotated corpora, and more powerful machine learning tools, the field has recently seen several new developments. In this survey, we study today’s most prominent Meaning Representation Frameworks. We shed light on their theoretical properties, as well as on their practical research environment, i.e., on datasets, parsers, applications, and future challenges.- Anthology ID:
- 2024.naacl-long.159
- Original:
- 2024.naacl-long.159v1
- Version 2:
- 2024.naacl-long.159v2
- Volume:
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2877–2892
- Language:
- URL:
- https://aclanthology.org/2024.naacl-long.159
- DOI:
- 10.18653/v1/2024.naacl-long.159
- Cite (ACL):
- Zacchary Sadeddine, Juri Opitz, and Fabian Suchanek. 2024. A Survey of Meaning Representations – From Theory to Practical Utility. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 2877–2892, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- A Survey of Meaning Representations – From Theory to Practical Utility (Sadeddine et al., NAACL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.naacl-long.159.pdf