Enhancing Automatic PT Tagging for MEDLINE Citations Using Transformer-Based Models
Not on the map. Why?
Abstract (arXiv)
We investigated the feasibility of predicting Medical Subject Headings (MeSH) Publication Types (PTs) from MEDLINE citation metadata using pre-trained Transformer-based models BERT and DistilBERT. This study addresses limitations in the current automated indexing process, which relies on legacy NLP algorithms. We evaluated monolithic multi-label classifiers and binary classifier ensembles to enhance the retrieval of biomedical literature. Results demonstrate the potential of Transformer models to significantly improve PT tagging accuracy, paving the way for scalable, efficient biomedical indexing.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 37 references that are in the map. First posted on arXiv 3 Jun 2025.
Builds on
4 of 37 references.
References · 4
- 2020Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluationcited by 183
- 2017A Network Perspective on Stratification of Multi-Label Datacited by 37
- 2021Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distributioncited by 34
- 2021Stratified Sampling for Extreme Multi-Label Datacited by 7
Built on by
Not cited yet.