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    <titleInfo>
        <title>Efficient Stacked Dependency Parsing by Forest Reranking</title>
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    <name type="personal">
        <namePart type="given">Katsuhiko</namePart>
        <namePart type="family">Hayashi</namePart>
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    <name type="personal">
        <namePart type="given">Shuhei</namePart>
        <namePart type="family">Kondo</namePart>
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    <name type="personal">
        <namePart type="given">Yuji</namePart>
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    <abstract>This paper proposes a discriminative forest reranking algorithm for dependency parsing that can be seen as a form of efficient stacked parsing. A dynamic programming shift-reduce parser produces a packed derivation forest which is then scored by a discriminative reranker, using the 1-best tree output by the shift-reduce parser as guide features in addition to third-order graph-based features. To improve efficiency and accuracy, this paper also proposes a novel shift-reduce parser that eliminates the spurious ambiguity of arc-standard transition systems. Testing on the English Penn Treebank data, forest reranking gave a state-of-the-art unlabeled dependency accuracy of 93.12.</abstract>
    <identifier type="citekey">hayashi-etal-2013-efficient</identifier>
    <identifier type="doi">10.1162/tacl_a_00216</identifier>
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        <url>https://aclanthology.org/Q13-1012</url>
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        <date>2013</date>
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%0 Journal Article
%T Efficient Stacked Dependency Parsing by Forest Reranking
%A Hayashi, Katsuhiko
%A Kondo, Shuhei
%A Matsumoto, Yuji
%J Transactions of the Association for Computational Linguistics
%D 2013
%V 1
%I MIT Press
%C Cambridge, MA
%F hayashi-etal-2013-efficient
%X This paper proposes a discriminative forest reranking algorithm for dependency parsing that can be seen as a form of efficient stacked parsing. A dynamic programming shift-reduce parser produces a packed derivation forest which is then scored by a discriminative reranker, using the 1-best tree output by the shift-reduce parser as guide features in addition to third-order graph-based features. To improve efficiency and accuracy, this paper also proposes a novel shift-reduce parser that eliminates the spurious ambiguity of arc-standard transition systems. Testing on the English Penn Treebank data, forest reranking gave a state-of-the-art unlabeled dependency accuracy of 93.12.
%R 10.1162/tacl_a_00216
%U https://aclanthology.org/Q13-1012
%U https://doi.org/10.1162/tacl_a_00216
%P 139-150

Markdown (Informal)

[Efficient Stacked Dependency Parsing by Forest Reranking](https://aclanthology.org/Q13-1012) (Hayashi et al., TACL 2013)

• Efficient Stacked Dependency Parsing by Forest Reranking (Hayashi et al., TACL 2013)

ACL

• Katsuhiko Hayashi, Shuhei Kondo, and Yuji Matsumoto. 2013. Efficient Stacked Dependency Parsing by Forest Reranking. Transactions of the Association for Computational Linguistics, 1:139–150.