Word2Vec: one static embedding per word — 'bank' always has the same vector.

Word2Vec: one static embedding per word — 'bank' always has the same vector. BERT: contextual embeddings — 'bank' in 'river bank' vs 'bank account' gets different vectors based on the full sentence. For a domain corpus ranking, Word2Vec was sufficient (less ambiguous domain terms). For a document extraction pipeline contract extraction, contextual embeddings are critical.