Abstract
In this study, we propose a novel relation extraction method enhanced by large language models (LLMs). We incorporated three relation extraction models that leverage LLMs: (1) relation extraction via in-context few-shot learning with LLMs, (2) enhancing the sequence-to-sequence (seq2seq)-based full fine-tuned relation extraction by CoT reasoning explanations generated by LLMs, (3) enhancing the classification-based full fine-tuned relation extraction by entity descriptions that are automatically generated by LLMs. In the experiment, we shot that in-context few-shot learning with LLMs suffers in biomedical relation extraction tasks. We further show that entity explanations that are generated by LLMs can improve the performance of the classification-based relation extraction in the biomedical domain. Our proposed model achieved an F-score of 85.61% on the DDIExtraction-2013 dataset, which is competitive with the state-of-the-art models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Asada, M., Miwa, M., Sasaki, Y.: Enhancing drug-drug interaction extraction from texts by molecular structure information. In: Gurevych, I., Miyao, Y. (eds.) Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685. Association for Computational Linguistics, Melbourne, Australia (Jul 2018). https://doi.org/10.18653/v1/P18-2108, https://aclanthology.org/P18-2108
Asada, M., Miwa, M., Sasaki, Y.: Integrating heterogeneous knowledge graphs into drug-drug interaction extraction from the literature. Bioinformatics 39(1), btac754 (2022). https://doi.org/10.1093/bioinformatics/btac754
Asada, M., et al.: Using drug descriptions and molecular structures for drug-drug interaction extraction from literature. Bioinformatics 37(12), 1739–1746 (2020). https://doi.org/10.1093/bioinformatics/btaa907
Baxter, K., Preston, C.L.: Stockley’s Drug Interactions, vol. 495. Pharmaceutical Press, London (2010)
Beltagy, I., et al.: SciBERT: a pretrained language model for scientific text. In: Proceedings of EMNLP-IJCNLP 2019, pp. 3615–3620. Hong Kong, China (Nov 2019)
Chen, Q., et al.: An extensive benchmark study on biomedical text generation and mining with ChatGPT. Bioinformatics 39(9), btad557 (2023). https://doi.org/10.1093/bioinformatics/btad557
Chen, Q., et al.: Large language models in biomedical natural language processing: benchmarks, baselines, and recommendations. arXiv preprint arXiv:2305.16326 (2023)
Chen, Y.: Incomplete utterance rewriting as sequential greedy tagging. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Findings of the Association for Computational Linguistics: ACL 2023, pp. 7265–7276. Association for Computational Linguistics, Toronto, Canada (Jul 2023). https://doi.org/10.18653/v1/2023.findings-acl.456, https://aclanthology.org/2023.findings-acl.456
Chung, H.W., et al.: Scaling instruction-finetuned language models (2022)
Fisher, R.A., et al.: The design of experiments (1937)
Gu, Y., et al.: Domain-specific language model pretraining for biomedical natural language processing. ACM Trans. Comput. Healthc. (HEALTH) 3(1), 1–23 (2021)
Huguet Cabot, P.L., Navigli, R.: REBEL: relation extraction by end-to-end language generation. In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 2370–2381. Association for Computational Linguistics, Punta Cana, Dominican Republic (Nov 2021). https://doi.org/10.18653/v1/2021.findings-emnlp.204, https://aclanthology.org/2021.findings-emnlp.204
Lee, J., et al.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234–1240 (2019). https://doi.org/10.1093/bioinformatics/btz682
Liu, S., et al.: Drug-drug interaction extraction via convolutional neural networks. Comput. Math. Methods Med. 2016 (2016)
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1(8), 9 (2019)
Sackett, D.L.: Evidence-based medicine. In: Seminars in Perinatology, vol. 21, pp. 3–5. Elsevier (1997)
Sahu, S.K., Anand, A.: Drug-drug interaction extraction from biomedical texts using long short-term memory network. J. Biomed. Inform. 86, 15–24 (2018)
Segura-Bedmar, I., Martínez, P., Herrero-Zazo, M.: SemEval-2013 task 9 : extraction of drug-drug interactions from biomedical texts (DDIExtraction 2013). In: Manandhar, S., Yuret, D. (eds.) Second Joint Conference on Lexical and Computational Semantics (*SEM), vol. 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), pp. 341–350. Association for Computational Linguistics, Atlanta, Georgia, USA (Jun 2013). https://aclanthology.org/S13-2056
Shazeer, N., Stern, M.: Adafactor: adaptive learning rates with sublinear memory cost. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 4596–4604. PMLR (10–15 Jul 2018). https://proceedings.mlr.press/v80/shazeer18a.html
Team, G., et al.: Gemini: a family of highly capable multimodal models (2023)
Wadhwa, S., Amir, S., Wallace, B.: Revisiting relation extraction in the era of large language models. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 15566–15589. Association for Computational Linguistics, Toronto, Canada (Jul 2023). https://doi.org/10.18653/v1/2023.acl-long.868, https://aclanthology.org/2023.acl-long.868
Wishart, D.S., et al.: DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46(D1), D1074–D1082 (2017). https://doi.org/10.1093/nar/gkx1037
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Asada, M., Fukuda, K. (2024). Enhancing Relation Extraction from Biomedical Texts by Large Language Models. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2024. Lecture Notes in Computer Science(), vol 14736. Springer, Cham. https://doi.org/10.1007/978-3-031-60615-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-60615-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-60614-4
Online ISBN: 978-3-031-60615-1
eBook Packages: Computer ScienceComputer Science (R0)