针对医学和工业领域嵌套实体识别难题，提出新损失函数提升小样本与不平衡数据下的识别效果。
EIoU-EMC: A Novel Loss for Domain-specific Nested Entity Recognition
- 设计EIoU-EMC损失，融合边界与分类信息增强学习能力。
- 在三个生物医学数据集和一个工业数据集上均优于基线模型。
- 特别提升边界识别精度，适合低资源与类别不均衡场景。
近年来，研究多集中于通用命名实体识别任务，而特定领域的嵌套实体识别仍面临挑战，尤其在低资源与类别不平衡情况下，制约了其在医学和工业领域的广泛应用。本文提出一种新型损失函数EIoU-EMC，通过增强交并比（IoU）损失与多类损失的实现，充分融合实体边界与分类信息，从而提升模型在有限数据样本下的学习能力。为验证该方法的有效性，我们在三个生物医学命名实体识别数据集及自建的工业复杂设备维护文档数据集上进行实验。结果表明，相比强基线模型，所提方法在所有数据集上均表现优异，尤其在实体边界识别与分类任务中取得显著进步。代码已公开。
原文摘要 · Abstract (English)
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.
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