How do word embeddings work?

Word embeddings are numerical representations of words that capture their meanings and relationships. They convert words into vectors (arrays of numbers) in a high-dimensional space, where words with similar meanings or usage contexts are positioned closer together. For example, “cat” and “dog” might have vectors that point in similar directions, while “car” would be farther away. This approach solves the problem of traditional methods like one-hot encoding, which treats words as isolated symbols and ignores semantic connections. Popular techniques like Word2Vec, GloVe, and FastText create these embeddings by analyzing how words appear in large text corpora.

Embeddings are trained using algorithms that learn from word co-occurrence patterns. For instance, Word2Vec uses a neural network to predict surrounding words (skip-gram) or a target word from its context (CBOW). During training, the model adjusts word vectors to minimize prediction errors. If the word “bank” often appears near “river,” “money,” or “loan,” its embedding will reflect those associations. GloVe takes a different approach, constructing a co-occurrence matrix of how frequently words appear together and factorizing it to produce vectors. These methods ensure that words sharing contexts—like “happy” and "joyful"—end up with similar vector values, even if they never appear in the exact same sentence.

Developers use embeddings to improve NLP tasks like text classification or machine translation. Instead of starting from scratch, many use pre-trained embeddings (e.g., GloVe’s 300-dimensional vectors trained on Wikipedia) as input features for models. This saves computation time and leverages existing semantic knowledge. For example, in a sentiment analysis model, embeddings help distinguish that “fantastic” and “terrible” have opposite meanings, even if both are adjectives. Embeddings also enable mathematical operations on words: subtracting “man” from “king” and adding “woman” might yield a vector close to “queen.” While embeddings don’t explicitly encode grammar or logic, their ability to capture semantic relationships makes them a foundational tool for modern NLP systems.