Word embeddings are also "simply computed." If you use GloVe, then the vectors are obtained by factoring a matrix of co-occurence counts.

The difference between machine learning and "simple" feature extraction is mostly just in the choice of metaphors used to describe the computation, not in any fundamental properties.