Entity Linking Systems with BERT and Contextual Embeddings

In a world of unstructured text, identifying the exact real-world entity behind a word is a major challenge for search engines and recommendation systems. Entity linking solves this by mapping ambiguous mentions to structured knowledge bases. Through clear written explanations, practical code walk-throughs, and structured reading exercises, you will learn how to design, build, and evaluate your own entity linking pipelines.\n\nWhat you'll learn:\n- Understand the core terminology of entity linking, including mention detection, candidate generation, and disambiguation.\n- Apply transformer-based embeddings like BERT to capture the nuanced context of words in text.\n- Implement dense retrieval techniques using modern vector database concepts to generate candidate entities efficiently.\n- Practice building classification and ranking models to select the correct entity from a list of candidates.\n- Explore current best practices in managing knowledge bases and evaluating entity linking performance.\n\nYou will start with the absolute basics of language modeling and vector representations before progressing to hands-on architectural designs for candidate generation and disambiguation. The course concludes with practical strategies for scaling and maintaining your models. This course is designed for beginners in natural language processing, with no prior experience in entity linking required beyond basic Python familiarity. Start reading today to unlock the power of semantic text understanding.