Inspire the Large Language Model by External Knowledge on BioMedical Named Entity Recognition (2309.12278v1)
Abstract: 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.
- Biomedical entity recognition by detection and matching. arXiv preprint arXiv:2306.15736.
- Olivier Bodenreider. 2004. The unified medical language system (umls): integrating biomedical terminology. volume 32, pages D267–D270. Oxford University Press.
- The colorado richly annotated full text (craft) corpus: Multi-model annotation in the biomedical domain. Handbook of Linguistic annotation, pages 1379–1394.
- An activity phosphorylating tyrosine in polyoma t antigen immunoprecipitates. Cell, 18(4):925–933.
- Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT, pages 4171–4186.
- Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4):1234–1240.
- A unified mrc framework for named entity recognition. arXiv preprint arXiv:1910.11476.
- Chain of knowledge: A framework for grounding large language models with structured knowledge bases. arXiv preprint arXiv:2305.13269.
- Unified structure generation for universal information extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5755–5772.
- Aioner: all-in-one scheme-based biomedical named entity recognition using deep learning. Bioinformatics, 39(5):btad310.
- Xuezhe Ma and Eduard Hovy. 2016. End-to-end sequence labeling via bi-directional lstm-cnns-crf. arXiv preprint arXiv:1603.01354.
- Gpt-ner: Named entity recognition via large language models. arXiv preprint arXiv:2304.10428.
- Cross-type biomedical named entity recognition with deep multi-task learning. Bioinformatics, 35(10):1745–1752.
- Huner: improving biomedical ner with pretraining. Bioinformatics, 36(1):295–302.
- Hunflair: an easy-to-use tool for state-of-the-art biomedical named entity recognition. Bioinformatics, 37(17):2792–2794.
- Pubtator central: automated concept annotation for biomedical full text articles. Nucleic acids research, 47(W1):W587–W593.
- Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824–24837.
- Verify-and-edit: A knowledge-enhanced chain-of-thought framework. arXiv preprint arXiv:2305.03268.
- Universalner: Targeted distillation from large language models for open named entity recognition.
- Junyi Bian (6 papers)
- Jiaxuan Zheng (2 papers)
- Yuyi Zhang (9 papers)
- Shanfeng Zhu (9 papers)