Abstract
Many real-world NLP applications face the challenge of training an entity disambiguation model for a specific domain with a small labeling budget. In this setting there is often access to a large unlabeled pool of documents. It is then natural to ask the question: which samples should be selected for annotation? In this paper we propose a solution that combines feature diversity with low rank correction. Our sampling strategy is formulated in the context of bilinear tensor models. Our experiments show that the proposed approach can significantly reduce the amount of labeled data necessary to achieve a given performance.- Anthology ID:
- 2023.findings-emnlp.479
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7208–7215
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.479
- DOI:
- 10.18653/v1/2023.findings-emnlp.479
- Cite (ACL):
- Audi Primadhanty and Ariadna Quattoni. 2023. Entity Disambiguation on a Tight Labeling Budget. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 7208–7215, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Entity Disambiguation on a Tight Labeling Budget (Primadhanty & Quattoni, Findings 2023)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2023.findings-emnlp.479.pdf