Sheth: 0000-0002-0021-5293

Document Type



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.

Digital Object Identifier (DOI)

APA Citation

Bajaj, G., Kursuncu, U., Gaur, M., Lokala, U., Hyder, A., Parthasarathy, S., & Sheth, A. (2022). Knowledge-Driven Drug-Use NamedEntity Recognition with Distant Supervision. MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation, 140–144.