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Another interesting thing that was not mentioned was that the index can place semantically similar items close together. For example, look at Word2Vec. It "in
by bcheung 9y ago
Another interesting thing that was not mentioned was that the index can place semantically similar items close together.
For example, look at Word2Vec. It "indexes" / "hashes" words that are semantically similar to the same "bucket". This means that you can search for semantically similar data just by inspecting what is near by. This has all kinds of applications.
- jzoch 9y agoLocality sensitive hashing (what you describe) is really useful for clustering and doing nearest-neighbor searches. Its used in digital fingerprinting, genome sequencing, and other interesting areas.
- mjburgess 9y agoThat's what word2vec is trying to model, it cannot do it because natural language semantics are person-2-world relationships. It groups words together based on their distributional characteristics, and with the right parameter tuning, that can be a good-enough model of similarity.
- bra-ket 9y agoit's called semantic hashing: https://www.sciencedirect.com/science/article/pii/S0888613X08001813 https://www.sciencedirect.com/science/article/pii/S0888613X0... also see https://en.wikipedia.org/wiki/Sparse_distributed_memory https://en.wikipedia.org/wiki/Sparse_distributed_memory
- tebba-lebba 9y agoOh, cool idea. I had not heard the term Semantic Hashing before, but I'm going to do some research now. Thanks for sharing!