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