Bioinformatics (Oxford, England) · 2026
Biomedical concept recognition with error-aware negative-enhanced ranking framework.
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Liu Shanshan, Nishida Noriki, Cheng Fei, Utsuro Takehito, Matsumoto Yuji
Аннотация
MOTIVATION: 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.
RESULTS: 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.
AVAILABILITY AND IMPLEMENTATION: The code and data underlying this article are available in Github at https://github.com/sl-633/enr-recognizer or in Zenodo at https://doi.org/10.5281/zenodo.20730803.
RESULTS: 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.
AVAILABILITY AND IMPLEMENTATION: The code and data underlying this article are available in Github at https://github.com/sl-633/enr-recognizer or in Zenodo at https://doi.org/10.5281/zenodo.20730803.