Word embeddings are a popular technique in natural language processing (NLP) that involves representing words as numerical vectors in a high-dimensional space. These vectors are generated based on the distributional context of words in a large corpus of text data. Word embeddings capture semantic relationships between words, allowing similar words to have similar vector representations, which can be used for various NLP tasks such as sentiment analysis, machine translation, and text classification. Popular word embedding models include Word2Vec, GloVe, and FastText.