Enhancing Named Entity Recognition for immunology and immune-mediated disorders

Abstract

Introduction: Named Entity Recognition (NER) in the biomedical domain, particularly within immunology and immune-mediated disorders, presents unique challenges due to the presence of complex, nested, and overlapping entities. Existing NER systems often struggle with the specialized terminologies and contextual ambiguity of immunological texts, which limits their effectiveness in downstream biomedical applications.

Methods: To address these challenges, we propose a domain-specific NERframework that integrates structured span encoding and knowledge-guided decoding. The framework is designed to enhance recognition accuracy under low-resource and weak supervision conditions by combining a hierarchical span encoder (SpanStructEncoder) with a constraint-based decoding strategy (Contextual Constraint Decoding, CCD). We evaluate our model on three immunology-specific datasets: the NCBI Disease Corpus (immune-related diseases), SNPPhenA (genetic variants and phenotype associations), and HLA-SPREAD (HLA-disease and drug-response relations). These datasets were selected because they represent key immunological concepts such as cytokines, immune cell types, and genetic markers that underlie immune responses and disease mechanisms.

Results and discussion: Experimental results demonstrate that our model achieves consistent improvements in F1-score over strong biomedical baselines including BioGPT, BioLinkBERT, and SciFive. Our results confirm that incorporating structured span representations and ontology-aware decoding significantly improves entity extraction for immunology-related texts. The proposed framework provides a robust and interpretable solution for immunology-focused biomedical text mining, facilitating applications in literature curation, biomarker discovery, and clinical decision support.

Keywords: biomedical NLP; constraint-based decoding; immunology; named entity recognition; structural span encoding.