A type of embedding for textual data.
Google Research put out an influential paper 10 years ago describing an early embedding model they created called Word2Vec.
That paper is Efficient Estimation of Word Representations in Vector Space, dated 16th January 2013. It’s a paper that helped kick off widespread interest in embeddings.
Word2Vec is a model that takes single words and turns them into a list of 300 numbers. That list of numbers captures something about the meaning of the associated word.
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What’s interesting about this is that it’s not guaranteed that the term “backups” appeared directly in the text of those READMEs. The content is semantically similar to that phrase, but might not be an exact textual match.
We can call this Semantic Search.1
Footnotes
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Embeddings: what they are and why they matter, Simon Willson ↩