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Here’s a clean and concise overview of Named Entity Recognition (NER) — great for study materials, presentations, or teaching others:
🧾 Named Entity Recognition (NER)
📌 What is NER?
Named Entity Recognition is a fundamental task in Natural Language Processing (NLP) that involves identifying and classifying named entities in text into predefined categories like:
- 🧑 Person names
- 🌍 Locations
- 🏢 Organizations
- 📅 Dates and times
- 💰 Monetary values
- 🔢 Percentages, etc.
🧠 Why Is NER Important?
NER helps extract structured information from unstructured text, enabling:
- Information retrieval
- Search engines
- Resume parsing
- Financial document analysis
- Question answering systems
- Social media monitoring
🔍 How NER Works
- Tokenization – Splitting text into words (tokens)
- Part-of-speech tagging – Assigning grammatical roles
- Entity recognition – Classifying tokens (or spans) into entity types
NER is often framed as a sequence labeling problem.
🏷️ Common Entity Labels
| Entity Type | Example |
|---|---|
| PERSON | Elon Musk, Serena Williams |
| ORG | Google, United Nations |
| GPE (Geo-political entity) | France, New York |
| DATE | January 1st, 2025 |
| TIME | 3:00 PM |
| MONEY | $100, 500 euros |
| PERCENT | 75%, 10 percent |
🤖 Techniques Used in NER
| Method | Description |
|---|---|
| Rule-based | Hand-crafted rules and dictionaries |
| Statistical Models | CRF (Conditional Random Fields), HMM |
| Deep Learning | LSTM, BiLSTM + CRF |
| Transformer-based | BERT, RoBERTa fine-tuned for NER |
➡️ Transformer-based models are now state-of-the-art in NER.
🧪 Example: NER in Action
Input:
Apple Inc. was founded by Steve Jobs in Cupertino in 1976.
NER Output:
- Apple Inc. → ORG
- Steve Jobs → PERSON
- Cupertino → GPE
- 1976 → DATE
🛠️ Popular Tools and Libraries
- spaCy
- NLTK
- Stanford NER
- Flair
- Hugging Face Transformers
Would you like a code snippet (e.g., using spaCy or BERT for NER) or a diagram of how an NER model works?