3 ms·

Automatic Keyword Extraction from Text

• PaulHoule 11y ago
  If you want to make it across the "commerialization valley of death" you are going to have to sweep the floor with Alchemy API rather than simply "perform better in some circumstances"
  • jisaacso 11y ago
    Totally agree! Our goal was to work with Insight Data Science to build a lightweight, simple keyword extractor in 3 weeks.
• timClicks 11y ago
  The code's available too: https://github.com/lvsh/keywordfinder https://github.com/lvsh/keywordfinder
• calmhead 11y ago
  Have you considered using PLSA (Probabilistic latent semantic analysis)? Effectively, this method does very similiar thing as you aim to do - it chooses a set of words that represen a topic of a document.
  • jisaacso 11y ago
    Great suggestion. We've used LDA for topic modeling in the past. I'm a big fan of running word2vec and clustering.
    • bglazer 11y ago
      > I'm a big fan of running word2vec and clustering. Can you explain more about this?
      • jisaacso 11y ago
        Opentable gave a great presentation on this not too long ago, check it out http://www.slideshare.net/SudeepDasPhD/recsys-2015-making-meaningful-restaurant-recommendations-at-opentable http://www.slideshare.net/SudeepDasPhD/recsys-2015-making-me...