Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets
- Authors
- Lee, Su Ah; Oh, Seokjin; Jung, Woohwan
- Issue Date
- Oct-2023
- Publisher
- Association for Computational Linguistics (ACL)
- Citation
- EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings, pp 3269 - 3279
- Pages
- 11
- Indexed
- SCOPUS
- Journal Title
- EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings
- Start Page
- 3269
- End Page
- 3279
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/118833
- DOI
- 10.48550/arXiv.2310.11715
- 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. Code is available at https://github.com/sue991/CoFiNER. ©2023 Association for Computational Linguistics.
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