Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Su Ah | - |
dc.contributor.author | Oh, Seokjin | - |
dc.contributor.author | Jung, Woohwan | - |
dc.date.accessioned | 2024-04-23T04:02:59Z | - |
dc.date.available | 2024-04-23T04:02:59Z | - |
dc.date.issued | 2023-10 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/118833 | - |
dc.description.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. | - |
dc.format.extent | 11 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Association for Computational Linguistics (ACL) | - |
dc.title | Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.48550/arXiv.2310.11715 | - |
dc.identifier.scopusid | 2-s2.0-85184807940 | - |
dc.identifier.bibliographicCitation | EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings, pp 3269 - 3279 | - |
dc.citation.title | EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings | - |
dc.citation.startPage | 3269 | - |
dc.citation.endPage | 3279 | - |
dc.type.docType | Conference paper | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.identifier.url | https://arxiv.org/abs/2310.11715 | - |
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