EIoU-EMC: A Novel Loss for Domain-specific Nested Entity Recognition
This work addresses nested NER challenges for biomedical and industrial applications, but it is incremental as it builds on existing IoU and multiclass loss methods.
The paper tackles nested named entity recognition in low-resource, class-imbalanced biomedical and industrial domains by proposing the EIoU-EMC loss, which enhances entity boundary and classification learning, and demonstrates competitive performance on four datasets.
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.