EIoU-EMC: A Novel Loss for Domain-specific Nested Entity Recognition
Journal:
arXiv
Published Date:
Apr 19, 2025
Abstract
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.
