Enhancing Relation Extraction from Biomedical Texts by Large Language Models
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-60615-1_1
Reference22 articles.
1. 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
2. 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
3. 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
4. Baxter, K., Preston, C.L.: Stockley’s Drug Interactions, vol. 495. Pharmaceutical Press, London (2010)
5. 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)
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