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An SVM based voting algorithm with application to parse reranking

Published:31 May 2003Publication History

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.

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  • Published in

    cover image DL Hosted proceedings
    CONLL '03: Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
    May 2003
    213 pages

    Publisher

    Association for Computational Linguistics

    United States

    Publication History

    • Published: 31 May 2003

    Qualifiers

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