Semantic Role Labeling of NomBank Partitives

Adam Meyers, Advait Pravin Savant, John E. Ortega


Abstract
This article is about Semantic Role Labeling for English partitive nouns (5%/REL of the price/ARG1; The price/ARG1 rose 5 percent/REL) in the NomBank annotated corpus. Several systems are described using traditional and transformer-based machine learning, as well as ensembling. Our highest scoring system achieves an F1 of 91.74% using “gold” parses from the Penn Treebank and 91.12% when using the Berkeley Neural parser. This research includes both classroom and experimental settings for system development.
Anthology ID:
2025.coling-main.23
Volume:
Proceedings of the 31st International Conference on Computational Linguistics
Month:
January
Year:
2025
Address:
Abu Dhabi, UAE
Editors:
Owen Rambow, Leo Wanner, Marianna Apidianaki, Hend Al-Khalifa, Barbara Di Eugenio, Steven Schockaert
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
324–336
Language:
URL:
https://aclanthology.org/2025.coling-main.23/
DOI:
Bibkey:
Cite (ACL):
Adam Meyers, Advait Pravin Savant, and John E. Ortega. 2025. Semantic Role Labeling of NomBank Partitives. In Proceedings of the 31st International Conference on Computational Linguistics, pages 324–336, Abu Dhabi, UAE. Association for Computational Linguistics.
Cite (Informal):
Semantic Role Labeling of NomBank Partitives (Meyers et al., COLING 2025)
Copy Citation:
PDF:
https://aclanthology.org/2025.coling-main.23.pdf