Abstract
Despite the significant progress made in Natural Language Processing (NLP) thanks to deep learning techniques, efforts are still needed to model explicit, factual, and accurate meaning representation formalisms. In this article, we present a comparative table of ten formalisms that have been proposed over the last thirty years, and we describe and put forth our own, Meaning Representation for Application Purposes (MR4AP), developed in an industrial context with a definitive applicative aim.- Anthology ID:
- 2023.dmr-1.11
- Volume:
- Proceedings of the Fourth International Workshop on Designing Meaning Representations
- Month:
- June
- Year:
- 2023
- Address:
- Nancy, France
- Editors:
- Julia Bonn, Nianwen Xue
- Venues:
- DMR | WS
- SIG:
- SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 110–121
- Language:
- URL:
- https://aclanthology.org/2023.dmr-1.11
- DOI:
- Cite (ACL):
- Bastien Giordano and Cédric Lopez. 2023. MR4AP: Meaning Representation for Application Purposes. In Proceedings of the Fourth International Workshop on Designing Meaning Representations, pages 110–121, Nancy, France. Association for Computational Linguistics.
- Cite (Informal):
- MR4AP: Meaning Representation for Application Purposes (Giordano & Lopez, DMR-WS 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2023.dmr-1.11.pdf