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Transition-based dependency parsers generally use heuristic decoding algorithms but can accommodate arbitrarily rich feature representations. In this paper, we show that we can improve the accuracy of such parsers by considering even richer feature sets than those employed in previous systems. In the standard Penn Treebank setup, our novel features improve(More)
Beam-search and global models have been applied to transition-based dependency parsing, leading to state-of-the-art accuracies that are comparable to the best graph-based parsers. In this paper, we analyze the effects of global learning and beam-search on the overall accuracy and error distribution of a transition-based dependency parser. First, we show(More)
This paper presents experiments with WordNet semantic classes to improve dependency parsing. We study the effect of semantic classes in three dependency parsers, using two types of constituency-to-dependency conversions of the English Penn Treebank. Overall, we can say that the improvements are small and not significant using automatic POS tags, contrary to(More)
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