A Study on Machine Learning Approaches for Named Entity Recognition

Natural Language Processing (NLP) is one of the sub-parts of Artificial Intelligence which normally focuses on empowering computers to understand and operate on human languages and to get computers closer towards understanding a language as a human does. Named Entity Recognition (NER) is core of NLP systems. NER is a process of automatic identification of named entities in a given text or document. Named entities are real world objects or in general named entities are proper nouns like name of person, location, date and time expression etc. The recognition and extraction of real named entity is very important for solving difficulties in many research areas like Question Answering and Summarization Systems, Information Extraction, Machine Learning, Semantic Web Search and Bio-informatics, Video Annotation and many more. In this paper the major focus is given on comprehending different types of NER and approaches applied for NER especially different machine learning models used for identification of Named Entities.