Our work differs from past studies in that we attempt to quantify exactly the types of errors these parsers make, tie them to their theoretical expectations, and show that integrating graph-based and transition-based parsers not only increases overall accuracy, but does so directly exploiting the strengths of each system.
this is the first large-scale error analysis of modern data-driven dependency parsers.