Roy, ArpitaPan, Shimei2025-01-082025-01-082021-11Roy, Arpita, and Shimei Pan. “Incorporating Medical Knowledge in BERT for Clinical Relation Extraction.” In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, edited by Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih, 5357–66. Online and Punta Cana, Dominican Republic: Association for Computational Linguistics, 2021. https://doi.org/10.18653/v1/2021.emnlp-main.435.https://doi.org/10.18653/v1/2021.emnlp-main.435http://hdl.handle.net/11603/37193Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, November, 2021, Online and Punta Cana, Dominican RepublicIn recent years pre-trained language models (PLM) such as BERT have proven to be very effective in diverse NLP tasks such as Information Extraction, Sentiment Analysis and Question Answering. Trained with massive general-domain text, these pre-trained language models capture rich syntactic, semantic and discourse information in the text. However, due to the differences between general and specific domain text (e.g., Wikipedia versus clinic notes), these models may not be ideal for domain-specific tasks (e.g., extracting clinical relations). Furthermore, it may require additional medical knowledge to understand clinical text properly. To solve these issues, in this research, we conduct a comprehensive examination of different techniques to add medical knowledge into a pre-trained BERT model for clinical relation extraction. Our best model outperforms the state-of-the-art systems on the benchmark i2b2/VA 2010 clinical relation extraction dataset.10 pagesen-USAttribution 4.0 International CC BY 4.0https://creativecommons.org/licenses/by/4.0/Text