# Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets

arXiv: https://arxiv.org/abs/2310.11715 · HTML: https://mlsanity.com/paper/2310.11715/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 18 Oct 2023.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Muppet: Massive Multi-task Representations with Pre-Finetuning](https://mlsanity.com/paper/2101.11038/) (2021)
- [Few-NERD: A Few-Shot Named Entity Recognition Dataset](https://mlsanity.com/paper/2105.07464/) (2021)
- [CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning](https://mlsanity.com/paper/2109.07589/) (2021)
- [BERN2: an advanced neural biomedical named entity recognition and normalization tool](https://mlsanity.com/paper/2201.02080/) (2022)
- [An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling](https://mlsanity.com/paper/2109.13023/) (2021)
- [Few-shot Learning for Named Entity Recognition in Medical Text](https://mlsanity.com/paper/1811.05468/) (2018)
- [Parallel Instance Query Network for Named Entity Recognition](https://mlsanity.com/paper/2203.10545/) (2022)
- [Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data](https://mlsanity.com/paper/2106.08977/) (2021)
- [Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation](https://mlsanity.com/paper/2011.11851/) (2020)

## Built on by (most cited first, 1 of 1)

- [EIoU-EMC: A Novel Loss for Domain-specific Nested Entity Recognition](https://mlsanity.com/paper/2504.14203/) (2025)

## Abstract (arXiv)

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.