Both on the surface looks like we generate a low dimension representation of texts by hashing or vectoring them, were similar vectors will lie close in the vector space if embedded (in the embedding case) and similar hashes will be in the same bucket (in the LSH case). How are these different ? What am I missing ?
How do semantic textual similarity search based on techniques like LSH contrast with distributional semantics based embedding techniques?
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