Research Area:  Machine Learning

Abstract:

The enormous growth and availability of data poses a great challenge for extracting useful information from documents written in natural language. The information extraction task has become a vital activity in all domains. The process of identifying the names of organization, people, locations or other entities in text is called named entity recognition (NER). It is the subtask and plays an important part to discover and classify the names such as organization name, person name or the location. This is one of the trending fields and most important step in the natural language processing (NLP) for analysis of text. Research on NER changed a lot in the recent decade. NER can consequently examine the entire articles and reveal the individuals, associations, and spots talked about in text. Knowing the applicable labels for every single article help in naturally arranging the articles in all around characterized progressive systems and endorse smooth content disclosure. The pretension of this paper is to present survey on NER. The prime contribution of this research to present state-of-the-art NER is systematically reviewed according to techniques used in NER. This paper also provides tools, datasets, techniques, challenges and future directions in the field of NER with the aim of providing researchers the substantial knowledge for further work.

Keywords:  
Named Entity Recognition
Natural Language Processing
Deep Learning
Machine Learning

Author(s) Name:  Abhishek Sharma, Amrita, Sudeshna Chakraborty & Shivam Kumar

Publisher name:  Springer

DOI:  10.1007/978-981-16-3346-1_66

Volume Information:  pp 817–828

Paper Link:   https://link.springer.com/chapter/10.1007/978-981-16-3346-1_66