An SVM based voting algorithm with application to parse reranking

Authors:
Libin Shen;Aravind K. Joshi
Affiliations:
University of Pennsylvania, Philadelphia, PA;University of Pennsylvania, Philadelphia, PA
Venue:
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Year:
2003

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Abstract

This paper introduces a novel Support Vector Machines (SVMs) based voting algorithm for reranking, which provides a way to solve the sequential models indirectly. We have 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.