Knowledge-Driven Drug-Use NamedEntity Recognition with Distant Supervision
Knowledge-Driven Drug-Use NamedEntity Recognition with Distant Supervision
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DOI：
10.3233/shti220048
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发表时间：
2022-06
影响因子：
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通讯作者：
Goonmeet Bajaj;Ugur Kursuncu;Manas Gaur;Usha Lokala;A. Hyder;Srinivas Parthasarathy;Amit P. Sheth;Srinivasan Parthasa-rathy
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作者：
Goonmeet Bajaj;Ugur Kursuncu;Manas Gaur;Usha Lokala;A. Hyder;Srinivas Parthasarathy;Amit P. Sheth;Srinivasan Parthasa-rathy
由于命名实体识别（NER）在识别非结构化内容的关键要素方面至关重要，通用的NER工具在识别特定领域的实体（如药物使用和公共卫生领域）方面仍然存在局限性。对于此类具有重大影响的领域，在更精细的层面准确捕捉相关实体至关重要，因为这些信息会影响现实世界的进程。另一方面，在没有人工特征的情况下针对特定领域训练NER模型需要大量的标注数据，这在人力和时间方面成本很高。在本研究中，我们利用特定领域的本体进行远程监督，以减少人力需求，并训练纳入特定领域（如药物使用）外部知识的模型来识别特定领域的实体。我们在政府流行病学报告中捕捉与药物使用相关的实体及其趋势，F1分数提高了8%。
As Named Entity Recognition (NER) has been essential in identifying critical elements of unstructured content, generic NER tools remain limited in recognizing entities specific to a domain, such as drug use and public health. For such high-impact areas, accurately capturing relevant entities at a more granular level is critical, as this information influences real-world processes. On the other hand, training NER models for a specific domain without handcrafted features requires an extensive amount of labeled data, which is expensive in human effort and time. In this study, we employ distant supervision utilizing a domain-specific ontology to reduce the need for human labor and train models incorporating domain-specific (e.g., drug use) external knowledge to recognize domain specific entities. We capture entities related the drug use and their trends in government epidemiology reports, with an improvement of 8% in F1-score.