Consequently, as part of our investigation, we propose a new corpus-based method for achieving global optimization and experiment with representing anaphoricity as a feature in the coreference system.
we systematically evaluate all four combinations of local vs. global optimization and constraint-based vs. feature-based representation of anaphoricity information in terms of their effectiveness in improving a learning-based coreference system.