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        <title>Generalized chart constraints for efficient PCFG and TAG parsing</title>
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    <name type="personal">
        <namePart type="given">Stefan</namePart>
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        <namePart type="given">Sophie</namePart>
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    <name type="personal">
        <namePart type="given">Alexander</namePart>
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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    <abstract>Chart constraints, which specify at which string positions a constituent may begin or end, have been shown to speed up chart parsers for PCFGs. We generalize chart constraints to more expressive grammar formalisms and describe a neural tagger which predicts chart constraints at very high precision. Our constraints accelerate both PCFG and TAG parsing, and combine effectively with other pruning techniques (coarse-to-fine and supertagging) for an overall speedup of two orders of magnitude, while improving accuracy.</abstract>
    <identifier type="citekey">grunewald-etal-2018-generalized</identifier>
    <identifier type="doi">10.18653/v1/P18-2099</identifier>
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        <date>2018-07</date>
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%0 Conference Proceedings
%T Generalized chart constraints for efficient PCFG and TAG parsing
%A Grünewald, Stefan
%A Henning, Sophie
%A Koller, Alexander
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F grunewald-etal-2018-generalized
%X Chart constraints, which specify at which string positions a constituent may begin or end, have been shown to speed up chart parsers for PCFGs. We generalize chart constraints to more expressive grammar formalisms and describe a neural tagger which predicts chart constraints at very high precision. Our constraints accelerate both PCFG and TAG parsing, and combine effectively with other pruning techniques (coarse-to-fine and supertagging) for an overall speedup of two orders of magnitude, while improving accuracy.
%R 10.18653/v1/P18-2099
%U https://aclanthology.org/P18-2099
%U https://doi.org/10.18653/v1/P18-2099
%P 626-631

Markdown (Informal)

[Generalized chart constraints for efficient PCFG and TAG parsing](https://aclanthology.org/P18-2099) (Grünewald et al., ACL 2018)

• Generalized chart constraints for efficient PCFG and TAG parsing (Grünewald et al., ACL 2018)

ACL

• Stefan Grünewald, Sophie Henning, and Alexander Koller. 2018. Generalized chart constraints for efficient PCFG and TAG parsing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 626–631, Melbourne, Australia. Association for Computational Linguistics.