How Word Embeddings Enable Computers to Grasp Meaning from Text

Computers process language by converting words into numerical vectors, a method known as word embeddings. These vectors are not manually assigned but are learned from analyzing how words are used in context. The core principle is that words appearing in similar contexts, like 'cat' and 'dog', receive similar numerical representations. This allows machine learning models to recognize semantic relationships. Advanced techniques like Word2Vec scale this concept by training on vast amounts of text to produce meaningful vector relationships.