Abstract
This paper begins with the premise that adverbs are neglected in computational linguistics. This view derives from two analyses: a literature review and a novel adverb dataset to probe a state-of-the-art language model, thereby uncovering systematic gaps in accounts for adverb meaning. We suggest that using Frame Semantics for characterizing word meaning, as in FrameNet, provides a promising approach to adverb analysis, given its ability to describe ambiguity, semantic roles, and null instantiation.- Anthology ID:
- 2023.starsem-1.44
- Volume:
- Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Alexis Palmer, Jose Camacho-collados
- Venue:
- *SEM
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 512–526
- Language:
- URL:
- https://aclanthology.org/2023.starsem-1.44
- DOI:
- 10.18653/v1/2023.starsem-1.44
- Cite (ACL):
- Dmitry Nikolaev, Collin Baker, Miriam R. L. Petruck, and Sebastian Padó. 2023. Adverbs, Surprisingly. In Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023), pages 512–526, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Adverbs, Surprisingly (Nikolaev et al., *SEM 2023)
- PDF:
- https://preview.aclanthology.org/landing_page/2023.starsem-1.44.pdf