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        <title>Incorporating medical knowledge in BERT for clinical relation extraction</title>
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        <namePart type="given">Arpita</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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            <namePart type="family">Yih</namePart>
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    <abstract>In 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.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.435</identifier>
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%0 Conference Proceedings
%T Incorporating medical knowledge in BERT for clinical relation extraction
%A Roy, Arpita
%A Pan, Shimei
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F roy-pan-2021-incorporating
%X In 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.
%R 10.18653/v1/2021.emnlp-main.435
%U https://aclanthology.org/2021.emnlp-main.435
%U https://doi.org/10.18653/v1/2021.emnlp-main.435
%P 5357-5366

Markdown (Informal)

[Incorporating medical knowledge in BERT for clinical relation extraction](https://aclanthology.org/2021.emnlp-main.435) (Roy & Pan, EMNLP 2021)

• Incorporating medical knowledge in BERT for clinical relation extraction (Roy & Pan, EMNLP 2021)

ACL

• Arpita Roy and Shimei Pan. 2021. Incorporating medical knowledge in BERT for clinical relation extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5357–5366, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.