Word Embeddings

Word embeddings are a way for computers to understand human language by turning words into numerical vectors (lists of numbers). These vectors capture the meaning of words based on their context and usage in large text data. Words with similar meanings have similar vectors, enabling computers to recognize relationships like synonyms or antonyms. This helps in tasks like translation, search, and sentiment analysis by allowing machines to process language more like humans do, understanding subtle connections and meanings beyond just matching words.