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An SVM-based voting algorithm with application to parse reranking

This paper introduces a novel Support Vector Machines (SVMs) based voting algorithm for reranking, which provides a way to solve the sequential models indirectly. presented a risk formulation under the PAC framework for this voting algorithm. We have applied this algorithm to the parse reranking problem, and achieved labeled recall and precision of 89.4%/89.8% on WSJ section 23 of Penn Treebank.