Word embeddings are a type of representation for words in a continuous vector space, where each word is mapped to a unique point in that space. This allows words with similar meanings to be located closer together, capturing semantic relationships. For example, the words "king" and "queen" would be positioned near each other, reflecting their related meanings. These embeddings are typically generated using algorithms like Word2Vec or GloVe, which analyze large text corpora to learn word associations. By transforming words into numerical vectors, word embeddings enable machines to better understand and process human language, improving tasks like natural language processing and machine learning.