A Reranking Approach for Dependency Parsing with Variable-sized Subtree Features
نویسندگان
چکیده
Employing higher-order subtree structures in graph-based dependency parsing has shown substantial improvement over the accuracy, however suffers from the inefficiency increasing with the order of subtrees. We present a new reranking approach for dependency parsing that can utilize complex subtree representation by applying efficient subtree selection heuristics. We demonstrate the effectiveness of the approach in experiments conducted on the Penn Treebank and the Chinese Treebank. Our system improves the baseline accuracy from 91.88% to 93.37% for English, and in the case of Chinese from 87.39% to 89.16%.
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