In natural language processing, named entity recognition is the key constituent for many tasks. Recent advancement in named entity recognition utilizes neural architectures that help accomplish remarkable performance with low-feature engineering.

Knowledge-based systems, unsupervised and bootstrapped systems, feature-engineered supervised systems, and feature-inferring neural network systems are some of the modern neural architecture-based named entity recognition systems. Named entity recognition in natural language processing has a variety of application areas, such as healthcare, biomedical research, cyber-security, aeronautics, and social media analysis.

More recently, deep learning techniques have been employed in named entity recognition to reduce the cost of human engineering for building domain-specific attributes and rules. Deep learning can impart continuous real-valued vector representations and semantic composition via nonlinear processing and produce state-of-the-art performance.

The effectively applied deep learning strategies for named entity recognition are Neural Attention, Deep Reinforcement Learning, Deep Adversarial Learning, Deep Active Learning, Deep Transfer Learning, and Deep Multi-task Learning. Named entity recognition facilitates various tasks to conduct huge amounts of digital information in structured and unstructured forms. Some popular complex natural language applicative tasks of named entity recognition are enumerated below;

•  Information Extraction: Information extraction plays a vital role in named entity recognition. Recent information extractions in named entity detection are Protein-Protein Interaction Information Extraction and event extraction.

•  Text Clustering: In knowledge discovery and data mining, text clustering is majorly used by clustering up the data of common features. Integrating the named entities and keywords aims to enhance the quality of text clustering. Recently, named entity recognition has attained outstanding development in suffix tree clustering.

•  Question-Answering: The question-answering system more effectively discovers the answers of various fact-based questions using named entity recognition.

•  Information Retrieval: Robust-named entity recognition analyzes the task of information retrieval problems by retaining accurate information. A content-based retrieval system has been implemented by integrating named entity recognition and semantic role labeling.

•  Machine Translation: Automated machine translation is conducted using named entity recognition with correct named entities. The named entity recognition improved the quality of the machine translation system.

•  Knowledgebase or ontology population: Constructing knowledge bases or ontologies includes extracting entities from data and learning the semantic and conceptual interconnection between them with the support of Relation Extraction and Named Entity Recognition.

•  Automatic Text Summarization: Named entities are important expressions for automatic text summarization and enhanced performance. Presently, considerable weight-assisted named entities are used in text summarization to resolve repetition problems.

•  Opinion mining: Named entity recognition advances the process of opinion mining for extracting features of products and analyzing the relevant opinions.

•  Semantic search: To make semantic search more influential and robust, named entity recognition in web search queries.

•  Other applications: Named entity recognition is efficaciously applied in a biomedical domain, such as the interaction between drug-drug, detection of adverse drug effects, classification of diagnosis, interactions between gene-gene and protein-protein, identification of heart disease risk factors, and extraction of biomedical entities.