Abstract
Accurate recognition of biomedical named entities is critical for medical information extraction and knowledge discovery. However, existing methods often struggle with nested entities, entity boundary ambiguity, and cross-lingual generalization. In this paper, we propose a unified Biomedical Named Entity Recognition (BioNER) framework based on Large Language Models (LLMs). We first reformulate BioNER as a text generation task and design a symbolic tagging strategy to jointly handle both flat and nested entities with explicit boundary annotation. To enhance multilingual and multi-task generalization, we perform bilingual joint fine-tuning across multiple Chinese and English datasets. Additionally, we introduce a contrastive learning-based entity selector that filters incorrect or spurious predictions by leveraging boundary-sensitive positive and negative samples. Experimental results on four benchmark datasets and two unseen corpora show that our method achieves state-of-the-art performance and robust zero-shot generalization across languages. The source codes are freely available at this https URL.
Abstract (translated)
生物医学命名实体的准确识别对于医疗信息提取和知识发现至关重要。然而，现有方法在处理嵌套实体、边界模糊以及跨语言泛化方面常常遇到困难。本文提出了一种基于大型语言模型（LLMs）的统一生物医学命名实体识别（BioNER）框架。首先，我们将BioNER重新定义为一个文本生成任务，并设计了一种符号标记策略，以明确标注边界的双重方式处理扁平和嵌套实体。为了增强多语言和多任务泛化能力，我们在多个中文和英文数据集上进行了双语联合微调。此外，我们引入了一个基于对比学习的实体选择器，通过利用边界敏感的正负样本来过滤错误或虚假预测。 在四个基准数据集和两个未见过的数据集中进行的实验结果表明，我们的方法实现了最先进的性能，并且具有跨语言的稳健零样本泛化能力。源代码可在此URL免费获取。
URL
https://arxiv.org/abs/2510.08902
