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Enhancing Named Entity Recognition for immunology and immune-mediated disorders
Songyue Chen1, Jinshan Che2, Mingming Sun2
1Department of Rheumatology and Immunology, First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
Frontiers in Immunology
|February 20, 2026
Summary
We developed a novel Named Entity Recognition (NER) framework to improve the extraction of immunological terms from complex biomedical texts. This approach enhances accuracy, especially in low-resource settings, aiding biomarker discovery and clinical support.
Area of Science:
- Biomedical informatics
- Computational biology
- Immunology
Background:
- Biomedical Named Entity Recognition (NER) faces challenges with complex, nested, and overlapping entities in immunology.
- Existing NER systems struggle with specialized terminology and contextual ambiguity in immunological texts, limiting downstream applications.
Purpose of the Study:
- To propose a domain-specific NER framework tailored for immunology.
- To enhance recognition accuracy under low-resource and weak supervision conditions.
Main Methods:
- Developed a framework integrating structured span encoding (SpanStructEncoder) and knowledge-guided decoding (Contextual Constraint Decoding, CCD).
- Evaluated the model on three immunology-specific datasets: NCBI Disease Corpus, SNPPhenA, and HLA-SPREAD.
Main Results:
- The proposed model consistently outperformed strong biomedical baselines (BioGPT, BioLinkBERT, SciFive) in F1-score.
- Incorporating structured span representations and ontology-aware decoding significantly improved entity extraction for immunology texts.
Conclusions:
- The framework offers a robust and interpretable solution for immunology-focused biomedical text mining.
- Facilitates applications in literature curation, biomarker discovery, and clinical decision support.
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