An analysis on various types of Deep learning Techniques used for Named Entity Recognition System

Abstract

Due to growing superficial solutions to the Natural language processing domains such as named
entity recognition for bio-medical, low resource languages and for annotated corpora where we can see the
tremendous increase of the use of named entity systems in these fields for proper accurate results. This
Named entity recognition (NER)is likely the initial move towards data extraction that hopes to discover and
gather named classes in content into pre-described classes, for instance, the names of individuals,
affiliations, regions, enunciations of times, amounts, money related qualities, rates, and so forth. NER is
utilized in numerous fields in Natural Language Processing, and where it can help to respond to some
certifiable inquiries. In this survey, we majorly discuss and give a brief description of the recently named
entity recognition which is built by the usage of deep learning techniques. This survey examines the papers
which used the named entity recognition system for the contextualization of named entity corpora,
predicting the diseases in the biomedical field and problems faced for low resource multilingual languages
that have been already given before. In this, we compared the existing methodologies which present in the
papers to check which could give a better result or accuracy by using this Named entity recognition