We present Pro3Gres, a deep-syntactic, fast dependency parser that combines a handwritten competence grammar with probabilistic performance disambiguation and that has been used i...
Gerold Schneider, Kaarel Kaljurand, Fabio Rinaldi,...
We describe our submission to the domain adaptation track of the CoNLL07 shared task in the open class for systems using external resources. Our main finding was that it was very...
The Conference on Computational Natural Language Learning features a shared task, in which participants train and test their learning systems on the same data sets. In 2007, as in...
We describe some challenges of adaptation in the 2007 CoNLL Shared Task on Domain Adaptation. Our error analysis for this task suggests that a primary source of error is differenc...
Mark Dredze, John Blitzer, Partha Pratim Talukdar,...
We present a data-driven variant of the LR algorithm for dependency parsing, and extend it with a best-first search for probabilistic generalized LR dependency parsing. Parser act...