NER (Named Entity Recognition) Tagging is a technique in Natural Language Processing (NLP) used to identify and classify key information (entities) in a text.
What Does “NER Tagging” Mean?
As mentioned above, NER mean Named Entity Recognition tagging. It helps extract structured data from unstructured text by categorizing words into predefined labels such as names, locations, organizations, dates, and more.
How Does it Work?
- Text Input – The system receives raw text
- Tokenization – The text is broken down into words or phrases
- Entity Recognition & Classification – Each recognized entity is labeled under a category
- Output – The structured data is used for further processing or analysis
Example of NER Tagging in a Sentence
Input Sentence: “Apple Inc. was founded by Steve Jobs in California in 1976.”
NER Output:
Apple Inc. → (Organization)
Steve Jobs → (Person)
California → (Location)
1976 → (Date)
Popular NER Tools & Libraries
- spaCy – Fast and efficient Python NLP library
- NLTK (Natural Language Toolkit) – A classic NLP toolkit for entity recognition
- Stanford NLP – Advanced linguistic analysis tool
- Google Cloud Natural Language API – Cloud-based NLP solution
- Amazon Comprehend – AWS NLP service for entity extraction
NER tagging is an essential NLP technique that extracts valuable information from text and helps in various applications, such as search engines, AI chatbots, and business intelligence.
