Embeddings are dense vector representations of data, often used to capture relationships in high-dimensional spaces. In NLP, word embeddings like Word2Vec or GloVe represent words as vectors, encoding semantic and syntactic information. For example, "king" and "queen" have similar embeddings, with differences capturing gender.
Embeddings are trained by optimizing tasks like predicting neighboring words in a sentence (Skip-gram) or filling in missing words (BERT). These pre-trained embeddings can then be fine-tuned for specific applications like sentiment analysis.
Beyond NLP, embeddings are useful in recommendation systems, clustering, and dimensionality reduction. For instance, user and item embeddings can represent preferences in collaborative filtering systems, enhancing personalized recommendations.