4 ms·

that's a well-studied subject. check out CEPT [1], Word2vec [2] (demo implementation [3]) and Wikipedia Miner [4]. [1] http://www.cept.at/demo_retina_viewer.ht

• dzink 13y ago
  This is very helpful. The odd part is some of these consider cancer as closely associated with health as it is with outer space. My goal is to connect people and projects with related people and projects who might be relevant to them. A user interested in healthcare might find someone working on a cancer project very relevant. With enough data we can create a structured process that correlates topics, but until then it seems the context behind each term in the libraries I'm seeing is very different. Better than nothing, but surprisingly different from what we want to do.
  • parsabg 13y ago
    > The odd part is some of these consider cancer as closely associated with health as it is with outer space. wikipedia miner fails at that one -- CEPT performs better. also with CEPT it's important to consider where the similarities are happening in the 2d matrix. in data mining problems the data sometimes becomes equally/more important as/than the underlying algorithm/technique so it's hard to tell what would work best for you without knowing more about your application. feel free to drop me an email if you'd like to discuss: parsa {at} aylien {dot} com