Cracking Word Embeddings: Why One-Hot Fails and How Word2Vec Actually Works

Natural Language Processing requires converting text into numerical formats because computers cannot natively process meaning. Traditional methods like bag-of-words, TF-IDF, and One-Hot Encoding face significant limitations, specifically high dimensionality sparsity and a total lack of semantic connection between related words. Word embeddings and the Word2Vec framework solve these challenges by mapping words into dense, continuous vectors. This self-supervised approach allows machine learning models to capture actual meaning and infer relationships that sparse vectors cannot represent.