🤖 Named Entity Recognition (NER) Tutorial

What is NER?

Named Entity Recognition is a key task in Natural Language Processing that identifies and classifies named entities (e.g., people, organizations, locations) in text.
Example:

• Input: "Apple Inc. was founded by Steve Jobs in 1976."
• Output: Apple_Inc (ORG), Steve_Jobs (PER), 1976 (DATE)

📚 Applications of NER

• Information Extraction: Extracting key facts from documents
• Question Answering: Identifying entities in queries
• Sentiment Analysis: Linking entities to contextual meanings
• Data Annotation: Labeling entities for training models

🧠 How NER Works

1. Tokenization: Split text into words
2. Feature Extraction: Use POS tags, word shapes, etc.
3. Model Training: Supervised learning with annotated datasets
4. Prediction: Classify entities using models like CRF or BERT

🛠️ Tools & Libraries

• spaCy Tutorial 📚
• NLTK Guide 🧩
• Transformers Library 🧪

🌐 Further Reading

Check our Introduction to NLP for foundational concepts.