{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/enhancing-clinical-bert-embedding-using-a","title":"Enhancing Clinical BERT Embedding using a Biomedical Knowledge Base","arxiv_id":null,"date":"2020-12-01","proceeding":"COLING 2020 8","authors":["Boran Hao","Henghui Zhu","Ioannis Paschalidis"],"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.","url_abs":"https://aclanthology.org/2020.coling-main.57","url_pdf":"https://aclanthology.org/2020.coling-main.57.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"enhancing-clinical-bert-embedding-using-a","repo_url":"https://github.com/noc-lab/clinical-kb-bert","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}