I can't edit anymore, so I'll reply to my own post: People are pointing out there is no known hash function that will map every word within a same distance D t

I can't edit anymore, so I'll reply to my own post: People are pointing out there is no known hash function that will map every word within a same distance D to the same hash. That is correct, and I was just meaning to explain the process at a high level. Levenshtein-locality sensitive hashing is not a solved problem in CS. And you know what else in the algorithm I described is not a solved problem? Efficient nearest neighbor search. In a space with more than a few dimensions, we actually don't know any better way to perform exact NNS than brute forcing the search space. And you know what? None of that matter, since we have inexact yet statistically excellent processes to perform both of these tasks. That's the really cool thing about engineering as opposed to math or computer science: it doesn't have to be exact to be useful. A trivial way to build an inexact, yet useful levenshtein hash function is to pick a random sample of words from your dictionary, define them as hash values, and assign to any input the closest word from your sample. Now work with a large family (100+) of such grossly approximate hashes and you can build a statistically excellent algorithm. (and of course this is just an example, there are countless better ways to do the same thing).