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    <titleInfo>
        <title>Learning Structural Kernels for Natural Language Processing</title>
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    <name type="personal">
        <namePart type="given">Daniel</namePart>
        <namePart type="family">Beck</namePart>
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        <namePart type="given">Trevor</namePart>
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    <name type="personal">
        <namePart type="given">Christian</namePart>
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    <name type="personal">
        <namePart type="given">Lucia</namePart>
        <namePart type="family">Specia</namePart>
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    <abstract>Structural kernels are a flexible learning paradigm that has been widely used in Natural Language Processing. However, the problem of model selection in kernel-based methods is usually overlooked. Previous approaches mostly rely on setting default values for kernel hyperparameters or using grid search, which is slow and coarse-grained. In contrast, Bayesian methods allow efficient model selection by maximizing the evidence on the training data through gradient-based methods. In this paper we show how to perform this in the context of structural kernels by using Gaussian Processes. Experimental results on tree kernels show that this procedure results in better prediction performance compared to hyperparameter optimization via grid search. The framework proposed in this paper can be adapted to other structures besides trees, e.g., strings and graphs, thereby extending the utility of kernel-based methods.</abstract>
    <identifier type="citekey">beck-etal-2015-learning</identifier>
    <identifier type="doi">10.1162/tacl_a_00151</identifier>
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        <url>https://aclanthology.org/Q15-1033</url>
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        <date>2015</date>
        <detail type="volume"><number>3</number></detail>
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            <start>461</start>
            <end>473</end>
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%0 Journal Article
%T Learning Structural Kernels for Natural Language Processing
%A Beck, Daniel
%A Cohn, Trevor
%A Hardmeier, Christian
%A Specia, Lucia
%J Transactions of the Association for Computational Linguistics
%D 2015
%V 3
%I MIT Press
%C Cambridge, MA
%F beck-etal-2015-learning
%X Structural kernels are a flexible learning paradigm that has been widely used in Natural Language Processing. However, the problem of model selection in kernel-based methods is usually overlooked. Previous approaches mostly rely on setting default values for kernel hyperparameters or using grid search, which is slow and coarse-grained. In contrast, Bayesian methods allow efficient model selection by maximizing the evidence on the training data through gradient-based methods. In this paper we show how to perform this in the context of structural kernels by using Gaussian Processes. Experimental results on tree kernels show that this procedure results in better prediction performance compared to hyperparameter optimization via grid search. The framework proposed in this paper can be adapted to other structures besides trees, e.g., strings and graphs, thereby extending the utility of kernel-based methods.
%R 10.1162/tacl_a_00151
%U https://aclanthology.org/Q15-1033
%U https://doi.org/10.1162/tacl_a_00151
%P 461-473

Markdown (Informal)

[Learning Structural Kernels for Natural Language Processing](https://aclanthology.org/Q15-1033) (Beck et al., TACL 2015)

• Learning Structural Kernels for Natural Language Processing (Beck et al., TACL 2015)

ACL

• Daniel Beck, Trevor Cohn, Christian Hardmeier, and Lucia Specia. 2015. Learning Structural Kernels for Natural Language Processing. Transactions of the Association for Computational Linguistics, 3:461–473.