What is Named Entity Recognition?
Also known as: NER
Named Entity Recognition (NER) is a natural language processing task that automatically finds and classifies specific names and terms in text into categories like people, organizations, locations, or dates.
NER works by processing text at the word or subword level and assigning labels to spans that represent entities. Models are typically trained on annotated datasets where humans have marked entity boundaries and types.
Common approaches include sequence labeling with conditional random fields, recurrent neural networks, or modern transformer-based models that capture context from the entire sentence to improve accuracy.
Key challenges include handling ambiguous names, nested entities, and domain-specific terminology, which often requires fine-tuning on specialized data.
Example
In the sentence "Elon Musk founded SpaceX in California in 2002", NER would tag "Elon Musk" as a person, "SpaceX" as an organization, "California" as a location, and "2002" as a date.
Why it matters
NER is a core building block for search engines, chatbots, recommendation systems, and automated document processing, enabling machines to extract structured knowledge from unstructured text at scale.
Frequently asked questions
No. NER specifically identifies and categorizes named entities rather than just any important words.
Related terms
An embedding (or vector embedding) is a way to represent words, sentences, or other data as dense numerical vectors in a high-dimensional space so that similar items end up close together.
Greedy decoding is a text generation strategy in NLP where, at each step, the model selects the single token with the highest probability as the next output.