Enhanced Cluster Merging and Deep Learning Techniques for Entity Name Identification from Biomedical Corpus

Abstract

For mining biomedical information identifying names is the prime task. Complex and uncertain naming styles of biomedical entities are the major setbacks here. Thus, state-of-the-art accuracy of biomedical name identification is reasonably inferior compared to general domain. This study includes machine learning and deep learning techniques to recognize names from biomedical corpus. In supervised classification, a classifier is built by finding required statistics from training corpus. Accordingly, performance of the system is primarily dependent on quantity and quality of training corpus. But manually preparing a large training dataset with enriched feature samples is laborious and time-taking. Therefore, various techniques were adopted in the literature to make effective use of raw corpora. We have incorporated a novel Cluster Merging technique and Attention Mechanism with BERT embedding for boosting machine learning and deep learning classifiers respectively. The suggested results outpour that profound techniques are competent and delineate signifying improvement over surviving methods.