One of the fascinating things about neural embedding such as these is that the individual component dimensions have NO "real" semantic meaning to us humans. It

One of the fascinating things about neural embedding such as these is that the individual component dimensions have NO "real" semantic meaning to us humans. It's better to think of them as single points in a higher dimensional space. (of course, with a clever network design you could probably FORCE "meaning" onto some components)