Abstract
Named Entity Recognition (NER) frequently suffers from the problem of insufficient labeled data, particularly in fine-grained NER scenarios. Although K-shot learning techniques can be applied, their performance tends to saturate when the number of annotations exceeds several tens of labels. To overcome this problem, we utilize existing coarse-grained datasets that offer a large number of annotations. A straightforward approach to address this problem is pre-finetuning, which employs coarse-grained data for representation learning. However, it cannot directly utilize the relationships between fine-grained and coarse-grained entities, although a fine-grained entity type is likely to be a subcategory of a coarse-grained entity type. We propose a fine-grained NER model with a Fine-to-Coarse(F2C) mapping matrix to leverage the hierarchical structure explicitly. In addition, we present an inconsistency filtering method to eliminate coarse-grained entities that are inconsistent with fine-grained entity types to avoid performance degradation. Our experimental results show that our method outperforms both K-shot learning and supervised learning methods when dealing with a small number of fine-grained annotations.- Anthology ID:
- 2023.emnlp-main.197
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3269–3279
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.197
- DOI:
- 10.18653/v1/2023.emnlp-main.197
- Cite (ACL):
- Su Lee, Seokjin Oh, and Woohwan Jung. 2023. Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 3269–3279, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets (Lee et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2023.emnlp-main.197.pdf