How Word Embeddings Map Relationships in AI Vector Spaces
Word embeddings translate human language into numerical vectors, allowing artificial intelligence models to understand semantic relationships and context. By mapping words into a multidimensional space based on usage, algorithms can determine similarities and analogies. This fundamental technique powers modern natural language processing, enabling machines to comprehend the nuances of human communication more effectively.
Source: Towards Data Science