The paper shows that iterative pseudo-labeling with confidence, class-adaptive, or GPT-4o filtering can improve fine-grained PICO NER under 10% labeled data, though gains are modest and code is not released.
Perspective and future of evidence-based medicine
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CL 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition
The paper shows that iterative pseudo-labeling with confidence, class-adaptive, or GPT-4o filtering can improve fine-grained PICO NER under 10% labeled data, though gains are modest and code is not released.