Named Entity Recognition, or NER, is an NLP technique that detects and classifies important entities in text. Entities can include:
- Names of people
- Locations (cities, countries)
- Organizations
- Dates and times
- Monetary amounts
NER helps machines extract meaningful information from unstructured text automatically.
How NER Works
- Text Preprocessing: Tokenize text and clean it.
- Entity Detection: Identify potential entities using rule-based or ML approaches.
- Classification: Assign the correct category (person, location, date, etc.).
- Postprocessing: Refine predictions to improve accuracy.
Modern NER often uses deep learning models like BERT for higher accuracy.
Advantages of NER
- Automates information extraction from large documents
- Reduces manual effort in processing text
- Improves search and question-answering systems
- Enables knowledge graph construction
Real-World Examples
- News analysis: Extract people, places, organizations
- Customer support: Identify product names in tickets
- Finance: Extract monetary values and dates
- Medical text: Identify diseases, drugs, treatments
- Search engines: Improve query understanding
Conclusion
NER is a powerful NLP tool that helps machines understand and organize textual information by extracting key entities, making AI smarter and more useful.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/