Global Learning of Labeled Dependency Trees
Michael Schiehlen, Kristina Spranger · Empirical Methods in Natural Language Processing · 2007
In the paper we describe a dependency parser that uses exact search and global learning (Crammer et al., 2006) to produce labelled dependency trees. Our system integrates the task of learning tree structure and learning labels in one step, using the same set of features for both tasks. During label prediction, the system automatically selects for each feature an appropriate level of smoothing. We report on several experiments that we conducted with our system. In the shared task evaluation, it scored better than average.