4 ms·

You don’t even need embeddings or Ml. A simple search across dictionaries, thesauruses and the Wikipedia entry list (with disambiguations) should be enough. -

• gwern 1y ago
  That would be a good baseline. Maybe the ML part would be for ranking them? Because I expect you would drown in matches, and the hard part becomes finding the good ones.
  • _1tem 1y ago
    I expect the opposite. I would expect an ML/embedding approach to find lots of false positives, because lots of words have close embeddings but are not synonyms. A strict thesaurus lookup should produce fewer matches. As for ranking the good ones, an embedder might help with that, but then we need a definition of "good". I would argue the most conceptually "unrelated" matches are the "best" ones, so yes, an embedder could quickly determine the farthest vector distance.