A Survey on Efficient Extraction of Named Entities from New Domains Using Big Data Analytics

Abstract

Named Entity Recognition (NER) is an important step in text mining. This paper proposes a survey on extracting named entities from new domains using big data analytics. The survey shows the methods and challenges applied for the efficient extraction of named entities from various fields having large corpus of data such as banking, medical and social networks. Most of NER methods discussed below are normally based on supervised learning techniques which often require a large amount of training dataset to train a good classifier. However, many applications in Information Retrieval (IR) and Natural Language Processing (NLP) suffer high from the noisy and short nature of texts.