Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) that identifies and classifies entities in text into predefined categories such as Persons, Organizations, Locations, Dates, and Quantities. It plays a crucial role in applications like information extraction, chatbots, and data mining.

Key Applications of NER

  • Information Extraction: Extract structured data from unstructured text (e.g., extracting company names from news articles).
  • Question Answering: Helps identify key entities in questions and answers.
  • Sentiment Analysis: Recognizes entities related to opinions or topics.
  • Machine Translation: Improves translation accuracy by preserving entity context.

Common Techniques

  1. Rule-Based Approaches: Use predefined patterns and dictionaries.
  2. Machine Learning Models: Train classifiers (e.g., CRF, SVM) on annotated datasets.
  3. Deep Learning: Leverage models like BERT, LSTM, or Transformers for state-of-the-art performance.

Example:

For the sentence "Apple was founded by Steve Jobs in 1976 in Cupertino.", NER would identify:

  • Apple → Organization
  • Steve Jobs → Person
  • Cupertino → Location
  • 1976 → Date

Explore more about NLP fundamentals here.

Named_Entity_Recognition
Natural_Language_Processing
Entity_Recognition_Example