Understanding Word Embeddings: How Machines Learn the Meaning of Words

Word embeddings are essential in Natural Language Processing (NLP) for enabling computers to understand human language. Since machines process numbers better than text, word embeddings represent words as numerical vectors. Instead of treating words as character sequences, this method uses lists of numbers, often containing hundreds of dimensions. These vectors allow machine-learning models to work mathematically, helping them recognize relationships between words, such as the similarity between cat and dog compared to cat and car.