Word Embedding
Dense vector representations of words that capture semantic meaning and relationships.
In-depth explanation
Word embeddings map words to continuous vector spaces where similar words are close together. Word2Vec, GloVe, and FastText learn embeddings from large text corpora. Embeddings capture semantic relationships: vector("king") - vector("man") + vector("woman") ≈ vector("queen"). Modern models use contextual embeddings that change based on surrounding words.
Examples
Related terms
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