Biomedical Entity Recognition Using a Small Language Model
Abstract
This research proposes developing a computationally efficient Small Language Model (SLM) for Biomedical Named Entity Recognition (NER). By fine-tuning a general-domain pretrained SLM (based on selective state space models, SSMs) on specialized biomedical texts, we aim to achieve performance comparable to larger models. Our approach addresses limitations of Transformer-based models like BioBERT (110M+ parameters) by combining linear-time sequence processing with biomedical domain adaptation techniques.
Fields of Study & Subjects
Biomedical Text Mining and Ontologies
Topic Modeling
Machine Learning in Healthcare
Publication details
- Journal / source
- RECIBE REVISTA ELECTRÓNICA DE COMPUTACIÓN INFORMÁTICA BIOMÉDICA Y ELECTRÓNICA
- Volume
- 14
- Issue
- 3
- Publication date
- 13 January 2026