How to learn Vector Semantics in NLP with real world examples | GenAI course by Srinivasan Ramanujam

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Vector semantics is a powerful tool in NLP that represents the meaning of words as vectors in a high-dimensional space. By leveraging the distributional hypothesis and word embeddings, it captures the semantic relationships between words based on their context. This approach has widespread applications, from search engines and recommendation systems to sentiment analysis and chatbots. A real-time example in a recipe app showed how vector semantics can recommend similar ingredients by calculating the proximity of their vectors in the semantic space, making it an invaluable approach for modern AI-based language tasks

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