Word2Vec (2013)

Word2Vec is a pioneering embedding model from 2013 that was the first to convert words into numeric vectors by learning from the contexts in which words appear. Word2Vec's main limitation was that it assigned each word one fixed vector; the word 'bank' had the same vector whether referring to a financial institution or a riverbank.

Only BERT (2018) and the Transformer architecture solved this problem by creating contextual vectors that depend on surrounding words. Word2Vec is important as the foundation of all embedding technology that today powers semantic search, clustering, and duplicate detection in SEO. Though outdated, the concept of 'word as vector' remains central — modern models (Jina, Gemini text-embedding-004) simply do the same thing but with context awareness and multilingual support.

By analogy, Word2Vec is like black-and-white photography: landmark in its time, but modern models are 4K photos with depth of field.