(2018) used low-depth decision trees to evaluate a method for learning feature directions in vector space embeddings.
if F = {f 1 , ..., f m } is the set of features that were identified, then they represent each entity e using the feature vector (d f 1 • e, ..., d fm • e), with d f the direction modelling feature f as before.