A Unified Biomedical Named Entity Recognition Framework with Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908585663922176 |
|---|---|
| author | Lv, Tengxiao Luo, Ling Li, Juntao Wang, Yanhua Pan, Yuchen Liu, Chao Wang, Yanan Jiang, Yan Lv, Huiyi Sun, Yuanyuan Wang, Jian Lin, Hongfei |
| author_facet | Lv, Tengxiao Luo, Ling Li, Juntao Wang, Yanhua Pan, Yuchen Liu, Chao Wang, Yanan Jiang, Yan Lv, Huiyi Sun, Yuanyuan Wang, Jian Lin, Hongfei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08902 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Unified Biomedical Named Entity Recognition Framework with Large Language Models Lv, Tengxiao Luo, Ling Li, Juntao Wang, Yanhua Pan, Yuchen Liu, Chao Wang, Yanan Jiang, Yan Lv, Huiyi Sun, Yuanyuan Wang, Jian Lin, Hongfei Computation and Language Artificial Intelligence 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. |
| title | A Unified Biomedical Named Entity Recognition Framework with Large Language Models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.08902 |