Named Entity Recognition using Deep Learning: A Review

The Recurrent neural network (RNN) has reported impressive performance on processing requirements in sequence labeling tasks. RNN can recall previously series knowledge and can thereby be utilized to solve tasks related to natural language processing (NLP). Recognition of a named entity (NER) is a popular NLP activity and can be found a classification challenge. Recognition of named entities is the method of recognizing various entities in a particular context. The role of retrieving chemical names from biomedical texts is Biomedical Named Entity Recognition (BNER) to enable biomedical and translational work. The purpose of the method is to retrieve valuable chemical names without many other engineering characteristics from of the content biomedical research. In this work, a study is performed on a number of deep learning techniques to investigate the robust identification of NER. In specific problems setups and implementations, case studies are performed to identify the most influential approaches for current implemented data mining techniques and it was found that recurrent neural network provides efficient modeling to form various types of NER.