Entity Extraction Techniques | Rule-Based, Machine Learning, and Hybrid Approaches скачать в хорошем качестве

Entity Extraction Techniques | Rule-Based, Machine Learning, and Hybrid Approaches

Entity extraction is a core Natural Language Processing (NLP) task used to identify structured information—such as names, dates, locations, and organizations—from unstructured text. In this video, we break down the three major entity extraction techniques used in real-world AI systems: 🔹 Rule-based approaches – Simple, transparent methods built using predefined patterns and domain expertise 🔹 Machine learning approaches – Data-driven models that learn entity recognition from large labeled datasets, including deep learning techniques like RNNs and Transformers (e.g., BERT) 🔹 Hybrid approaches – Practical systems that combine rules and ML to achieve higher accuracy and flexibility You’ll learn: How each technique works Their strengths and limitations When to use each approach in production systems Real-world examples of hybrid entity extraction pipelines This session is ideal for AI engineers, NLP learners, data scientists, and backend/frontend developers working with text-heavy applications like search, chatbots, and document processing systems.