Improving Zero-Shot Multiclass Classification for Narrative Reports from National Violent Death Reporting System.

Journal: Studies in health technology and informatics
Published Date: Aug 7, 2025

Abstract

The natural language processing pipeline powered by the BART model is a popular zero-shot text classification system. While the standard approach for using this pipeline can achieve impressive accuracies in many multiclass classification tasks, we believed that there was still room to improve and developed an improved approach for that. Both approaches were used for the classification of narrative reports, and the results showed that the improved approach could increase the accuracies significantly over the standard approach. The improved approach is made general and can be applied to other use cases as well.

Authors

  • Yijun Shao
    Veterans Affairs Medical Center, Washington, DC; George Washington University, Washington, DC.
  • Ryan Wu
    George Washington University, Washington, DC, USA.
  • Adnan Lakdawala
    George Washington University, Washington, DC, USA.
  • Alicia M Jones
    Northwestern University, Chicago, IL, USA.
  • Megan Koch
    Illinois Department of Public Health, Springfield, IL, USA.
  • Yingxuan Liu
    Perceptual and Cognitive Systems, TNO, Soesterberg, The Netherlands.
  • Lori A Post
    Northwestern University, Chicago, IL, USA.
  • Maryann Mason
    Northwestern University, Chicago, IL, USA.
  • Qing Zeng-Treitler
    Veterans Affairs Medical Center, Washington, DC; George Washington University, Washington, DC.

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