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    <abstract>Domain knowledge is important for building Natural Language Processing (NLP) systems for low-resource settings, such as in the clinical domain. In this paper, a novel joint training method is introduced for adding knowledge base information from the Unified Medical Language System (UMLS) into language model pre-training for some clinical domain corpus. We show that in three different downstream clinical NLP tasks, our pre-trained language model outperforms the corresponding model with no knowledge base information and other state-of-the-art models. Specifically, in a natural language inference task applied to clinical texts, our knowledge base pre-training approach improves accuracy by up to 1.7%, whereas in clinical name entity recognition tasks, the F1-score improves by up to 1.0%. The pre-trained models are available at https://github.com/noc-lab/clinical-kb-bert.</abstract>
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%0 Conference Proceedings
%T Enhancing Clinical BERT Embedding using a Biomedical Knowledge Base
%A Hao, Boran
%A Zhu, Henghui
%A Paschalidis, Ioannis
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F hao-etal-2020-enhancing
%X Domain knowledge is important for building Natural Language Processing (NLP) systems for low-resource settings, such as in the clinical domain. In this paper, a novel joint training method is introduced for adding knowledge base information from the Unified Medical Language System (UMLS) into language model pre-training for some clinical domain corpus. We show that in three different downstream clinical NLP tasks, our pre-trained language model outperforms the corresponding model with no knowledge base information and other state-of-the-art models. Specifically, in a natural language inference task applied to clinical texts, our knowledge base pre-training approach improves accuracy by up to 1.7%, whereas in clinical name entity recognition tasks, the F1-score improves by up to 1.0%. The pre-trained models are available at https://github.com/noc-lab/clinical-kb-bert.
%R 10.18653/v1/2020.coling-main.57
%U https://aclanthology.org/2020.coling-main.57/
%U https://doi.org/10.18653/v1/2020.coling-main.57
%P 657-661

Markdown (Informal)

[Enhancing Clinical BERT Embedding using a Biomedical Knowledge Base](https://aclanthology.org/2020.coling-main.57/) (Hao et al., COLING 2020)

• Enhancing Clinical BERT Embedding using a Biomedical Knowledge Base (Hao et al., COLING 2020)

ACL

• Boran Hao, Henghui Zhu, and Ioannis Paschalidis. 2020. Enhancing Clinical BERT Embedding using a Biomedical Knowledge Base. In Proceedings of the 28th International Conference on Computational Linguistics, pages 657–661, Barcelona, Spain (Online). International Committee on Computational Linguistics.