While early transition-based parsers generally used greedy best-first inference and locally trained classifiers, recent work has shown that higher accuracy can be obtained using beam search and global structure learning to mitigate error propagation.
it seems that the globally learned models can exploit a much richer feature space than locally trained classifiers, as shown by Zhang and Nivre (2011).