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We combine transitionbased dependency parsing with a high performing but relatively underexplored machine learning technique, Robust Risk Minimization. During decoding, we judiciously prune the next parsing states using kbest ranking. Moreover, we apply a simple postprocessing to ensure robustness. We evaluate our approach on the CoNLL’09 shared task English data and improve transitionbased dependency parsing accuracy, reaching a labeled attachment score of 89.28%. We also have observed near quadratic average running time in practice for the algorithm.
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