# EIoU-EMC: A Novel Loss for Domain-specific Nested Entity Recognition

arXiv: https://arxiv.org/abs/2504.14203 · HTML: https://mlsanity.com/paper/2504.14203/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 19 Apr 2025.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Circle Loss: A Unified Perspective of Pair Similarity Optimization](https://mlsanity.com/paper/2002.10857/) (2020)
- [UnitBox: An Advanced Object Detection Network](https://mlsanity.com/paper/1608.01471/) (2016)
- [Dice Loss for Data-imbalanced NLP Tasks](https://mlsanity.com/paper/1911.02855/) (2019)
- [Span-based Joint Entity and Relation Extraction with Transformer Pre-training](https://mlsanity.com/paper/1909.07755/) (2019)
- [CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark](https://mlsanity.com/paper/2106.08087/) (2021)
- [Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation](https://mlsanity.com/paper/2005.03572/) (2020)
- [Unified Named Entity Recognition as Word-Word Relation Classification](https://mlsanity.com/paper/2112.10070/) (2021)
- [Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition](https://mlsanity.com/paper/2105.06804/) (2021)
- [Neural Segmental Hypergraphs for Overlapping Mention Recognition](https://mlsanity.com/paper/1810.01817/) (2018)
- [Low-Resource Name Tagging Learned with Weakly Labeled Data](https://mlsanity.com/paper/1908.09659/) (2019)
- [Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?](https://mlsanity.com/paper/2212.10784/) (2022)
- [Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition](https://mlsanity.com/paper/2204.05544/) (2022)
- [AUC Maximization for Low-Resource Named Entity Recognition](https://mlsanity.com/paper/2212.04800/) (2022)
- [Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets](https://mlsanity.com/paper/2310.11715/) (2023)

## Abstract (arXiv)

In recent years, research has mainly focused on the general NER task. There still have some challenges with nested NER task in the specific domains. Specifically, the scenarios of low resource and class imbalance impede the wide application for biomedical and industrial domains. In this study, we design a novel loss EIoU-EMC, by enhancing the implement of Intersection over Union loss and Multiclass loss. Our proposed method specially leverages the information of entity boundary and entity classification, thereby enhancing the model's capacity to learn from a limited number of data samples. To validate the performance of this innovative method in enhancing NER task, we conducted experiments on three distinct biomedical NER datasets and one dataset constructed by ourselves from industrial complex equipment maintenance documents. Comparing to strong baselines, our method demonstrates the competitive performance across all datasets. During the experimental analysis, our proposed method exhibits significant advancements in entity boundary recognition and entity classification. Our code are available here.