Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) that involves identifying and classifying named entities in text into predefined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.

Key Concepts

  • Named Entities: Refers to words or phrases that refer to specific entities, like "Apple Inc." or "New York City".
  • Categories: Different types of entities have different categories. For example, a person's name would fall under the "Person" category.
  • Recognition: The process of identifying these entities in a given text.

How NER Works

NER systems typically use machine learning algorithms to predict the category of each word in a sentence. Here's a simplified overview:

  1. Tokenization: The text is broken down into individual words or tokens.
  2. Feature Extraction: Various features are extracted from the tokens, such as their part of speech, surrounding words, and context.
  3. Model Prediction: A machine learning model predicts the category of each token based on the extracted features.

Tools and Libraries

Several tools and libraries can be used for NER tasks, such as:

  • spaCy: An open-source library for advanced NLP tasks, including NER.
  • Stanford NER: A part of the Stanford CoreNLP suite, which is widely used in the academic community.

Example

Here's an example of how NER might work on a sentence:

Input: "Apple Inc. released the iPhone in 2007."
Output: "Apple Inc. (ORGANIZATION), iPhone (PRODUCT), 2007 (DATE)"

Further Reading

For those looking to dive deeper into NER, here's a recommended resource:

NER Diagram