Description:  
"The objective is to find an orthogonal matrix Q ∈R d×d such that EQ is interpretable, i.e., the values of the first k dimensions correlate well with the linguistic feature"
"By rotating word spaces, interpretable dimensions can be identified while preserving the information contained in the embeddings without any loss. In this work, we investigate three methods for making word spaces interpretable by rotation: Densifier (Rothe et al., 2016), linear SVMs and DensRay, a new method we propose. "