Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Authors

Shanshan Liu, Noriki Nishida, Fei Cheng, Utsuro Takehito, Yuji Matsumoto

Published in

Bioinformatics (Oxford, England). Jul 03, 2026. Epub Jul 03, 2026.

Abstract

Mention-agnostic biomedical concept recognition (MA-BCR) requires inferring ontology concepts directly from passages, without relying on explicit mention spans. Prior work has mainly focused on generative and classification-based approaches. Ranking-based methods typically use a retrieve-rerank pipeline, and this paradigm has not been systematically studied for MA-BCR. Consequently, it remains unclear how ranking-based approaches compare with existing paradigms and what types of supervision are most beneficial for ranker training under limited annotation settings.
Through a systematic comparison of ranking-, generative-, and classification-based paradigms, we show that a two-stage retrieve-rerank architecture is the most robust and scalable backbone for MA-BCR. Building on this finding, we propose ENR, an error-aware negative-enhanced ranking framework that augments training with false positives collected from heterogeneous recognizers, improving reranking performance without increasing inference-time cost. Experiments on MM-HPO and MM-GO (two datasets derived from MedMentions-ST21pv) demonstrate that ENR substantially outperforms prior approaches.
Code and data are available at https://github.com/sl-633/enr-recognizer or https://doi.org/10.5281/zenodo.20730803.
Supplementary materials are available at Supplemental_Materials.pdf.

PMID:
42398028
Bibliographic data and abstract were imported from PubMed on 04 Jul 2026.

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