BE - Natural Language Processing

Named Entity Recognition (NER)

Named Entity Recognition is a Natural Language Processing task that automatically identifies and classifies named entities (specific objects) in text into predefined categories like persons, organizations, locations, dates, etc. 
How it works: 

The system scans text and labels specific words or phrases with their corresponding entity types. 
Example: 

Input Text: "Apple Inc. was founded by Steve Jobs in Cupertino, California on April 1, 1976." 

NER Output: 

    Apple Inc. - ORGANIZATION
    Steve Jobs - PERSON
    Cupertino - LOCATION
    California - LOCATION
    April 1, 1976 - DATE
     

Common Entity Types: 

    PERSON - Names of people (John Smith, Marie Curie)
    ORGANIZATION - Companies, institutions (Google, Harvard University)
    LOCATION - Places, cities, countries (Paris, Amazon River)
    DATE - Calendar dates (January 1st, 2023)
    TIME - Times of day (3:30 PM, midnight)
    MONEY - Monetary values ($100, €50)
    PERCENT - Percentages (25%, 3.2%)
     

Real-world Applications: 

    Information extraction from news articles
    Customer service - extracting names, addresses from emails
    Medical records - identifying patient names, medications, dosages
    Social media monitoring - tracking brand mentions and locations
    Search engines - understanding query intent