Abstract
Human annotations in natural language corpora vary due to differing human perspectives. This is especially prevalent in subjective tasks. In these datasets, certain data samples are more prone to label variation and can be indicated as ambiguous samples.- Anthology ID:
- 2025.law-1.2
- Volume:
- Proceedings of the 19th Linguistic Annotation Workshop (LAW-XIX-2025)
- Month:
- July
- Year:
- 2025
- Address:
- Vienna, Austria
- Editors:
- Siyao Peng, Ines Rehbein
- Venues:
- LAW | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 21–34
- Language:
- URL:
- https://preview.aclanthology.org/more-markup/2025.law-1.2/
- DOI:
- 10.18653/v1/2025.law-1.2
- Cite (ACL):
- Richard Alies, Elena Merdjanovska, and Alan Akbik. 2025. Measuring Label Ambiguity in Subjective Tasks using Predictive Uncertainty Estimation. In Proceedings of the 19th Linguistic Annotation Workshop (LAW-XIX-2025), pages 21–34, Vienna, Austria. Association for Computational Linguistics.
- Cite (Informal):
- Measuring Label Ambiguity in Subjective Tasks using Predictive Uncertainty Estimation (Alies et al., LAW 2025)
- PDF:
- https://preview.aclanthology.org/more-markup/2025.law-1.2.pdf