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I came across two interesting papers which describe promising approaches for document classification using word embedding.

1. The fasttext algorithm

Described in the paper Bag of Tricks for Efficient Text Classification here.

(With further explanation here).

2. DANs

Described in the paper Deep Unordered Composition Rivals Syntactic Methods for Text Classification here.

Question:

What is the difference between both approaches?

Are they essentially the same as they both seem to average word embedding and pass it through a MLP or am I missing something crucial?

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    $\begingroup$ Until somebody takes the time to read both, here's my opinion: they might be; people independently invent similar algorithms often and it takes time for the dust to settle. I note that the first article is newer and not peer reviewed. If your interest is practical, I'd compare benchmarks and test them myself: fasttext, dan. $\endgroup$ – Emre Apr 2 '17 at 4:33
  • $\begingroup$ The first one (fasttext) is a standard method in the field, has 750 citations and the peer reviewed version can be found at aclweb.org/anthology/papers/E/E17/E17-2068. The second one sounds more fringe and is also quite old, anno 2015. And cs224n has a 2019 version, regarding the link in the original post. $\endgroup$ – BookYourLuck Apr 20 at 16:14
  • $\begingroup$ And the second one seems to be indeed merely a bag of words model with a fancy title. $\endgroup$ – BookYourLuck Apr 20 at 16:31

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