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Creators/Authors contains: "Bajaj, Goonmeet"

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  1. ; ; ; (, IEEE Intelligent Systems)
    Full Text Available 
  2. ; ; ; ; ; ; (, Studies in health technology and informatics)
    d 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. 
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    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; (, Topics in Cognitive Science)
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