arXiv 2510.089022025-10-10cs.CLcs.AI

基于大型语言模型的统一生物医学命名实体识别框架

A Unified Biomedical Named Entity Recognition Framework with Large Language Models

College of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；Air Force Communications NCO Academy（空军通信NCO学院）；Department of Pharmacy, Second Affiliated Hospital of Dalian Medical University（大连医科大学第二附属医院药学部）

Tengxiao Lv, Ling Luo, Juntao Li, Yanhua Wang, Yuchen Pan, Chao Liu, Yanan Wang, Yan Jiang, Huiyi Lv, Yuanyuan Sun, Jian Wang, Hongfei Lin

备注：: Accepted as a short paper at BIBM2025

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AI总结：

提出基于LLMs的统一BioNER框架，通过文本生成、符号标记、双语联合微调与对比学习实体选择器，解决嵌套实体与跨语言泛化问题，实现SOTA性能与鲁棒零样本泛化。

摘要：

准确识别生物医学命名实体对医学信息抽取和知识发现至关重要。然而，现有方法常难以处理嵌套实体、实体边界歧义及跨语言泛化。本文提出基于大型语言模型（LLMs）的统一生物医学命名实体识别（BioNER）框架。我们首先将BioNER重新表述为文本生成任务，并设计符号标记策略，通过显式边界标注联合处理扁平与嵌套实体。为增强多语言和多任务泛化能力，我们在多个中英文数据集上进行双语联合微调。此外，我们引入基于对比学习的实体选择器，利用边界敏感的正负样本过滤错误或虚假预测。在四个基准数据集和两个未见语料上的实验结果表明，该方法实现了最先进的性能及跨语言的鲁棒零样本泛化。源代码公开于https://github.com/dreamer-tx/LLMNER。

英文摘要：

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 https://github.com/dreamer-tx/LLMNER.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。