Our hypothesis, for which we provide empirical justification, is that in cases where there is no theoretically preferred annotation, NED prefers the structures that are more learnable by DMV.
nED gives higher scores to the annotations that better fit the assumptions and modeling decisions of DMV, the model that lies in the basis of the parsing algorithms.