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Comparing Multiple Models for Section Header Classification with Feature Evaluation
Ronak Pipaliya1, Paul M Heider2, Stéphane M Meystre2
1College of Medicine, Medical University of South Carolina, Charleston, SC, USC.
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.
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