Biomedical named entity recognition based on hybrid multistage cnn-rnn learner

Abstract

This current research presents an inventive multilevel named entity recognition scheme for explaining the confrontation with biomedical entity recognition which based on divergent algorithms. The presented scheme contains multilevels, which enables Biomedical entity recognition tasks to extract and identify important biomedical concept: DNA, RNA, CELL-LINE, CELL-TYPE, PROTEIN, and O classes with ease. The BioNLP/NLPBPA 2004 challenge datasets have been used and evaluated, resulted in promising outcomes in terms of biomedical recognition model performance.