Computational Semantics: Representing Meaning in Natural Language

How do computer programs actually understand the meaning behind human words? To build intelligent systems, we must bridge the gap between human language and computational logic. This text-only course guides you through the core paradigms of computational semantics, from classic relational networks to modern vector-based representations. You will learn how to structure word meanings, connect language to real-world context, and apply these concepts to modern natural language processing tasks. What you'll learn: - Understand the fundamental theories of how machines represent and process linguistic meaning - Explore relational methods and semantic networks to map logical connections between words - Examine semantic grounding to connect digital concepts with physical actions and perceptions - Analyze conceptual schemas and how agents build frameworks to reason about the world - Learn modern vector space models and word embeddings used in contemporary language technology - Practice designing semantic representations through written exercises and conceptual walkthroughs You will start with foundational definitions of meaning and semantics before progressing to structural models, grounding theories, and modern computational implementations. This course is designed for beginners curious about computational linguistics and natural language processing, with no prior programming or advanced math background required. Start reading today to unlock the mechanics of machine understanding.