Word Embeddings (e.g., Word2Vec, GloVe)

Word embeddings are techniques that transform words into numerical vectors, capturing their meanings and relationships. Models like Word2Vec and GloVe analyze large text datasets to learn how words co-occur with others. This way, words with similar meanings or usage contexts are represented by vectors close together in a multi-dimensional space. For example, “king” and “queen” would have similar vectors, reflecting their related concepts. These embeddings enable computers to understand language more effectively, improving tasks like translation, search, and sentiment analysis by providing a meaningful numerical representation of word meanings.