Technical GlossaryNatural Language Processing
Word2Vec
A historical embedding approach that represents word meaning through dense vectors learned from contextual co-occurrence.
Word2Vec was one of the landmark methods that made distributional semantics practical at scale. Its skip-gram and CBOW variants learn semantic structure from co-occurrence patterns among words. The resulting vectors carry strong signals for similarity, analogy, and clustering tasks. Before the era of contextual embeddings, it was one of the core technologies that reshaped the direction of NLP.
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