Named Entity Recognition (NER) is a subtask of information extraction that identifies named entities in text. Named entities are words or phrases that refer to specific things, such as people, organizations, locations, and dates.

What is NER?

NER is an important task in NLP because it allows machines to understand and extract valuable information from text. For example, NER can be used to extract the names of people and organizations from news articles, or to identify the locations mentioned in social media posts.

NER in Practice

Here's an example of how NER can be applied:

  • Input Text: "Apple Inc. is an American multinational technology company headquartered in Cupertino, California."
  • Output:
    • Apple Inc. (Organization)
    • American (Location)
    • Cupertino (Location)
    • California (Location)

Tools and Libraries

There are several tools and libraries available for NER, including:

  • spaCy: An open-source library for advanced NLP tasks.
  • Stanford NER: A tool developed by Stanford University for NER tasks.
  • NLTK: A Python library for NLP tasks, including NER.

For more information on NLP tools and libraries, visit our NLP Resources page.

Resources

NER Example