  We contribute to the discussion on parsing performance in NLP by introducing
a measurement that evaluates the differences between the distributions of edge
displacement (the directed distance of edges) seen in training and test data.
We hypothesize that this measurement will be related to differences observed in
parsing performance across treebanks. We motivate this by building upon
previous work and then attempt to falsify this hypothesis by using a number of
statistical methods. We establish that there is a statistical correlation
between this measurement and parsing performance even when controlling for
potential covariants. We then use this to establish a sampling technique that
gives us an adversarial and complementary split. This gives an idea of the
lower and upper bounds of parsing systems for a given treebank in lieu of
freshly sampled data. In a broader sense, the methodology presented here can
act as a reference for future correlation-based exploratory work in NLP.
