How Do Word Embeddings Capture Relationships Between Words?

Word embeddings represent words as numerical vectors, allowing models to understand semantic relationships based on context. These relationships are learned by analyzing large amounts of text, operating on the principle that words appearing in similar contexts have related meanings. The Word2Vec Skip-gram model, for instance, trains a network by taking a target word and trying to predict its surrounding context words. Through this process, the model adjusts each word's vector so that related words end up with similar representations in the vector space, enabling applications like semantic search.