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

Nested NER tasks have some challenges in specific domains, such as biomedical and industrial fields, particularly due to low resource and class imbalance, which impede its wide application. In this study, we design a novel loss EIoU-EMC, by enhancing the implement of Intersection over Union loss and Multi-class 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 and data are available at https://github.com/luminous11/EIoU-EMC/