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Exploring Biomedical Named Entity Recognition via SciSpaCy and BioBERT Models
Abstract
Introduction
Biological Named Entity Recognition (BioNER) is a crucial preprocessing step for Bio-AI analysis.
Methods
Our paper explores the field of Biomedical Named Entity Recognition (BioNER) by closely analysing two advanced models, SciSpaCy and BioBERT. We have made two distinct contributions: Initially, we thoroughly train these models using a wide range of biological datasets, allowing for a methodical assessment of their performance in many areas. We offer detailed evaluations using important parameters like F1 scores and processing speed to provide precise insights into the effectiveness of BioNER activities.
Results
Furthermore, our study provides significant recommendations for choosing tools that are customised to meet unique BioNER needs, thereby enhancing the efficiency of Named Entity Recognition in the field of biomedical research. Our work focuses on tackling the complex challenges involved in BioNER and enhancing our understanding of model performance.
Conclusion
The goal of this research is to drive progress in this important field and enable more effective use of advanced data analysis tools for extracting valuable insights from biomedical literature.
