Enchancing Enhancing Medical Named Entity Recognition with Ensemble Voting of BERT-Based Models on BC5CDR

The rapid development in biotechnology and medical research has resulted in a large amount of scientific literature containing critical information about various medical entities. However, the primary challenge in managing this data is the vast volume of unstructured text, which requires Natural Language Processing (NLP) techniques for automatic information extraction. One of the main applications in NLP is Named Entity Recognition (NER), which aims to identify important entities in the text, such as disease names, drugs, and proteins. This study aims to enhance the performance of medical Named Entity Recognition (NER) by applying ensemble Voting to three BERT-based models: BioBERT, TinyBERT, and ClinicalBERT. The results show that the ensemble voting technique provides the best performance in medical entity extraction, with improvements in precision (0.9494), recall (0.9483), and F1-score (0.9488) compared to individual models, especially when handling less common medical entities. This approach is expected to contribute to the development of automated systems for analyzing and searching information in medical literature.