Abstract
Categorial grammar (CG) is a lexicalized grammar formalism that can be used to identify and extract the semantics of natural language sentences. However, despite being used actively to solve natural language understanding tasks such as natural language inference or recognizing textual entailment, most of the tools exploiting the capacities of CG are available in a limited set of languages. This paper proposes a first step toward developing a set of tools enabling the use of CG for the French language by proposing a neural network tailored for part-of-speech and type-logical-grammar supertagging, located at the frontier between computational linguistics and artificial intelligence. Experiments show that our model can compete with state-of-the art models while retaining a simple architecture.- Anthology ID:
- 2023.iwcs-1.8
- Volume:
- Proceedings of the 15th International Conference on Computational Semantics
- Month:
- June
- Year:
- 2023
- Address:
- Nancy, France
- Editors:
- Maxime Amblard, Ellen Breitholtz
- Venue:
- IWCS
- SIG:
- SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 78–82
- Language:
- URL:
- https://aclanthology.org/2023.iwcs-1.8
- DOI:
- Cite (ACL):
- Gaëtan Margueritte, Daisuke Bekki, and Koji Mineshima. 2023. Multi-purpose neural network for French categorial grammars. In Proceedings of the 15th International Conference on Computational Semantics, pages 78–82, Nancy, France. Association for Computational Linguistics.
- Cite (Informal):
- Multi-purpose neural network for French categorial grammars (Margueritte et al., IWCS 2023)
- PDF:
- https://preview.aclanthology.org/revert-3132-ingestion-checklist/2023.iwcs-1.8.pdf