Word embeddings work by representing words as continuous, dense vectors, where each vector encodes semantic meaning. Unlike traditional one-hot encoding, which creates sparse vectors with only a single non-zero element, word embeddings allow words with similar meanings to have similar vector representations. This is achieved through training on a large corpus of text, where a model learns to predict the context of words in a sentence.
One popular method for generating word embeddings is Word2Vec, which uses a shallow neural network to predict surrounding words (context) for a given target word. There are two approaches within Word2Vec: Continuous Bag of Words (CBOW) and Skip-Gram. In CBOW, the model uses context words to predict the target word, while in Skip-Gram, the target word is used to predict the context. Through training, the model adjusts the weights in the neural network to create vectors that represent the semantic properties of words.
Another widely used approach is GloVe (Global Vectors for Word Representation), which uses matrix factorization to generate word embeddings based on the co-occurrence statistics of words across a corpus. Both Word2Vec and GloVe result in word embeddings that group similar words together in the vector space, making them highly useful for tasks like sentiment analysis, language translation, and information retrieval.