Named-Entity Recognition


Named-Entity Recognition (NER) is a subtask of Information Extraction that aims to identify and classify real-world entities, such as people, organizations, and locations, within a given text. It utilizes natural language processing (NLP) and machine learning techniques to achieve this goal. The process of NER enables computers to understand and extract valuable information from unstructured text for a variety of applications such as sentiment analysis, chatbots, and data mining.

Key Takeaways

  1. Named-Entity Recognition (NER) is a subtask of natural language processing (NLP) that involves identifying and classifying named entities such as people, organizations, locations, and various other entity types in a given text.
  2. NER is widely used in various applications, including machine translation, chatbots, recommendation systems, and sentiment analysis, to understand the context and extract relevant information.
  3. There are different techniques for NER, including rule-based methods, supervised and unsupervised machine learning models, as well as deep learning approaches like recurrent neural networks (RNNs) and transformers.


Named-Entity Recognition (NER) is an essential aspect of natural language processing (NLP) that focuses on identifying and classifying specific elements, such as names of people, organizations, locations, expressions of time, and quantities, within a given text.

This technology is significant because it enables machines to understand the context and extract relevant information from vast amounts of unstructured data, allowing for more accurate sentiment analysis, information retrieval, and data analytics, among other applications.

As a result, NER plays a critical role in enhancing the capabilities of various industries, such as finance, healthcare, and customer service, by providing them with valuable insights which aid in decision-making, trend prediction, and improving overall efficiency.


Named-Entity Recognition (NER) is a subfield within the broader domain of natural language processing (NLP) that centers around a critical goal: accurately identifying and classifying real-world entities, such as people, locations, and organizations, embedded within textual data. As a vital tool in NLP, NER serves to extract meaning and context from unstructured data sources, transforming raw text into structured forms that open up a world of possibilities for machine learning and data analysis applications.

The significance of NER goes beyond just identifying entities; its end purpose lies in deriving valuable insights from data and enhancing the efficiency of various tasks and processes across numerous industries. By fulfilling this purpose, NER has become indispensable across a wide range of applications such as search engines, sentiment analysis, knowledge extraction, chatbots, and data mining.

For example, news organizations can harness NER to track the prominence of named entities in their articles, which in turn can help predict trending topics or scrutinize journalistic biases. In the business sphere, NER is utilized for sentiment analysis, allowing companies to gauge consumers’ perception of their brand by discerning entities mentioned in customer reviews or social media interactions.

Ultimately, Named-Entity Recognition stands as a keystone technology in empowering both AI systems and human users to easily navigate, understand, and glean knowledge from ever-growing volumes of unstructured textual data.

Examples of Named-Entity Recognition

Named-Entity Recognition (NER) is a subtask of information extraction that focuses on identifying and classifying named entities like person names, organizations, locations, product names, and dates within a given text. Here are three real-world examples of NER applications:

News Article Classification: A popular use of NER is in the classification and organization of news articles. By identifying key named entities such as people, organizations, and locations within the text, news organizations can automatically tag articles with relevant categories, making them easily searchable and navigable. This also helps in content recommendation and targeted advertising.

Resume Parsing: Recruitment companies and job search platforms often use NER to extract pertinent information from resumes and job listings. By identifying named entities such as candidate names, job titles, company names, skills, and educational qualifications, these systems can match job seekers to relevant job openings, filter resumes, and organize candidate information in a structured manner.

Customer Support Automation: NER is used in customer support systems to understand customer queries more effectively and guide them to appropriate solutions. By recognizing entities such as product names, issues, and user information, the system can automatically route customer support tickets to relevant departments or provide quick automated responses. This enables companies to improve response time and customer satisfaction while reducing the workload on the support staff.

FAQ: Named-Entity Recognition

What is Named-Entity Recognition?

Named-Entity Recognition (NER) is a subtask of natural language processing that aims to identify and classify named entities in unstructured text. Named entities may include persons, organizations, locations, dates, and more. NER is widely used for information extraction, sentiment analysis, and intelligent systems development.

What are some common applications of Named-Entity Recognition?

NER has various applications, including sentiment analysis, information extraction, voice assistants, search engines, data mining, document classification, medical records analysis, news aggregation, and social media monitoring, among others.

How do machine learning models perform Named-Entity Recognition?

Machine learning models perform NER by training on large text corpuses labeled with named entities. They learn to recognize patterns and contextual associations between words and entities. Some popular machine learning algorithms for NER include Conditional Random Fields (CRFs), Recurrent Neural Networks (RNNs), and more recently, Transformer-based models such as BERT and RoBERTa.

What is the difference between Named-Entity Recognition and Text Classification?

While both NER and text classification are subtasks of natural language processing, they serve different purposes. NER focuses on identifying and extracting specific named entities within a text, like people, places, and organizations. Text classification, on the other hand, aims to determine the overall topic or category of a document or piece of text as a whole, rather than extracting specific entities.

Do I need a large training dataset for Named-Entity Recognition?

A large labeled dataset is usually necessary for training accurate and effective NER models. However, due to the advancements in transfer learning techniques and pre-trained models, you may still achieve satisfactory performance even with a smaller annotated dataset. By fine-tuning pre-trained models like BERT or RoBERTa on your specific named-entity recognition task, you can leverage the knowledge learned from large-scale pre-training to improve your results with smaller labeled datasets.

Related Technology Terms

  • Text Mining
  • Machine Learning
  • Information Extraction
  • Natural Language Processing (NLP)
  • Part-of-speech Tagging

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