Machine Translation Models and Named Entity Recognition: Comprehensive Study

Abstract

Human translation is no longer sufficient to meet society's needs as there are more regular international contacts. However, as computer technology advances, machine translation is becoming a practical option. Machine Translation (MT) is a tough and challenging process since natural languages differ in a multilingual environment. Named Entity Recognition is one of the major tasks of natural language processing because it recognizes predefined text meanings as language entities in the text in any language. Our inference from the examined literature is that a machine translation strategy along with named entity recognition is a more effective way in NLP than applying MT approaches alone. A hybrid technique, on the other hand, utilizes the advantages of two approaches to enhance the translation's overall quality and performance. This paper's objective is to provide a comprehensive report of machine translation models named entity recognition in general, and this article reviews the development of machine translation over time and examines its primary techniques before making recommendations for its design.