Inspire the Large Language Model by External Knowledge on BioMedical Named Entity Recognition

• URL: http://arxiv.org/abs/2309.12278v1
• Date: Thu, 21 Sep 2023 17:39:53 GMT
• Title: Inspire the Large Language Model by External Knowledge on BioMedical Named Entity Recognition
• Authors: Junyi Bian, Jiaxuan Zheng, Yuyi Zhang, Shanfeng Zhu
• Abstract summary: Large language models (LLMs) have demonstrated dominating performance in many NLP tasks, especially on generative tasks. We leverage the LLM to solve the Biomedical NER task into entity span extraction and entity type determination. Experimental results show a significant improvement in our two-step BioNER approach compared to previous few-shot LLM baseline.
• Score: 3.427366431933441
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have demonstrated dominating performance in many NLP tasks, especially on generative tasks. However, they often fall short in some information extraction tasks, particularly those requiring domain-specific knowledge, such as Biomedical Named Entity Recognition (NER). In this paper, inspired by Chain-of-thought, we leverage the LLM to solve the Biomedical NER step-by-step: break down the NER task into entity span extraction and entity type determination. Additionally, for entity type determination, we inject entity knowledge to address the problem that LLM's lack of domain knowledge when predicting entity category. Experimental results show a significant improvement in our two-step BioNER approach compared to previous few-shot LLM baseline. Additionally, the incorporation of external knowledge significantly enhances entity category determination performance.

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