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"
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.
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The code's available too: https://github.com/lvsh/keywordfinder
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"
Totally agree! Our goal was to work with Insight Data Science to build a lightweight, simple keyword extractor in 3 weeks.
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.
Great suggestion. We've used LDA for topic modeling in the past. I'm a big fan of running word2vec and clustering.
Can you explain more about this?
Opentable gave a great presentation on this not too long ago, check it out http://www.slideshare.net/SudeepDasPhD/recsys-2015-making-me...