Entity Naming in NLP: Hybrid Approach GPT Transformer and Multi-level RNN

This research addresses the limitations of existing entity-naming algorithms in natural language processing when the algorithm faces the complexities of polysemy and intricate sentence structures. We propose a novel Transformer-multi-level fusion recurrent Neural Network (T-MFRNN) model that integrates transformer-based layers for contextual understanding with a multi-level fusion recurrent neural network to capture temporal dependencies. Through rigorous experimentation on prominent NLP models like BERT, GPT2, ELECTRA, and XNet, the proposed T-MFRNN demonstrated a significant performance enhancement. Compared to existing methods, such as the boundary assembly model (BAM), the T-MFRNN exhibits a marked improvement of up to 18.66% in the F1-score, highlighting its superior ability to accurately label entities in complex biomedical texts. The T-MFRNN model’s outstanding performance underscores its potential as a robust solution for biomedical entity naming applications.