Graph-based parsers (Eisner, 1996;McDonald et al., 2005) are based on global optimization of models that work by scoring subtrees.
transition-based parsers (Yamada and Matsumoto, 2003;Nivre et al., 2004), which are the focus of this work, use local training to make greedy decisions that deterministically select the next parser state.