Published: Jan 23, 2025
We are proud to announce that our group will be at NAACL 2025 with 1 long paper in the Findings Track! Catch us in Albuquerque, New Mexico to learn more about open-domain biomedical NER.
OpenBioNER: Lightweight Open-Domain Biomedical Named Entity Recognition Through Entity Type Description
by A. Cocchieri, G. Frisoni, M. Martinez Galindo, G. Moro, G. Tagliavini, and F. Candoli
Biomedical Named Entity Recognition (BioNER) faces significant challenges in real-world applications due to limited annotated data and the constant emergence of new entity types, making zero-shot learning capabilities crucial. While Large Language Models (LLMs) possess extensive domain knowledge necessary for specialized fields like biomedicine, their computational costs often make them impractical. To address these challenges, we introduce OpenBioNER, a lightweight BERT-based cross-encoder architecture that can identify any biomedical entity using only its description, eliminating the need for retraining on new, unseen entity types. Through comprehensive evaluation on established biomedical benchmarks, we demonstrate that OpenBioNER surpasses state-of-the-art baselines, including specialized 7B NER LLMs and GPT-4o, achieving up to 10% higher F1 scores while using 110M parameters only. Moreover, OpenBioNER outperforms existing small-scale models that match textual spans with entity types rather than descriptions, both in terms of accuracy and computational efficiency.
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The paper will be available soon!