Comparing Multiple Models for Section Header Classification with Feature Evaluation.

Journal: Studies in health technology and informatics
Published Date: Jun 6, 2022

Abstract

We present on the performance evaluation of machine learning (ML) and Natural Language Processing (NLP) based Section Header classification. The section headers classification task was performed as a two-pass system. The first pass detects a section header while the second pass classifies it. Recall, precision, and F1-measure metrics were reported to explore the best approach for ML based section header classification for use in downstream NLP tasks.

Authors

  • Ronak Pipaliya
    College of Medicine, Medical University of South Carolina, Charleston, SC, USC.
  • Paul M Heider
    Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA.
  • Stéphane M Meystre
    Department of Biomedical Informatics, University of Utah, Salt Lake City, USA.

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