Named Entity Recognition (NER) is the process of identifying and categorizing named entities in a given text. Examples of categories are organizations, locations, time, names, money, and rate. Other terms that are synonymous to NER are:

  • Entity Identification
  • Entity Extraction
  • Entity Chunking

NER is part of information extraction (IE) or the process of automatically getting structured information from an unstructured document. With NER, the entity is the specific piece of information extracted. An example of NER is when the following unannotated text gets annotated:

Bill Gates sold $35.8 billion worth of Microsoft stock and gave it to the Bill and Melinda Gates Foundation.

NER creates the following annotated text from the sentence above:

[Bill Gates]Person sold [$35.8 billion]Money worth of Microsoft Stock and gave it to the [Bill and Melinda Gates Foundation]Organization.

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Named Entity Recognition first came to light in 1995 during the Message Understanding Conferences in the U.S. Back then, it was considered a subtask of IE. Today, however, NER is also used in Natural Language Processing (NLP), and it has been quite useful across many sectors. Below are some of the use cases of NER.

Real-World Applications of Named Entity Recognition (NER)

  • Content recommendation: When you read an article on a news website such as BBC and CNN, you would notice a list of articles on the side or below that are related to the one you’re reading. These websites use NER to extract entities from the article you’re reading and recommend others that contain information about them. For instance, if an article is about the coronavirus outbreak, you’d see a slew of other articles about the same topic.
  • Search algorithm: Have you ever wondered how sites that have millions of content can return relevant results when you search for something? Take Wikipedia, for example. When you search for “jobs,” instead of returning all articles with the word “jobs” in them, Wikipedia returns a page that contains predefined entities that the search term might refer to. Hence, Wikipedia suggests a link to the page where “occupation” is defined, a section for people named Jobs, another part for movies, video games, and other entertainment content where the word “jobs” appears. You would also see another section for places that contain the search term.
  • Ecommerce: Online stores that offer hundreds or thousands of products would benefit a lot if they use NER in their product search algorithm. Without NER, a search for “black stiletto boots” would show stilettos that aren’t boots, boots that aren’t stilettos, and stiletto boots that aren’t black. Ecommerce sites will lose customers if this is the case. NER would classify the search term in our example as black being the color and the stiletto boots as product type.
  • Customer support: Most customers these days tag a brand’s social media handle when complaining. For companies with branches all over the world, NER makes the job of the customer service department easier. All posts from customers can go through a scan for a location entity, and once found, the concern can get forwarded to the right branch.