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    <titleInfo>
        <title>Determining Semantic Textual Similarity using Natural Deduction Proofs</title>
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    <name type="personal">
        <namePart type="given">Hitomi</namePart>
        <namePart type="family">Yanaka</namePart>
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    <name type="personal">
        <namePart type="given">Koji</namePart>
        <namePart type="family">Mineshima</namePart>
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    <name type="personal">
        <namePart type="given">Pascual</namePart>
        <namePart type="family">Martínez-Gómez</namePart>
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    <name type="personal">
        <namePart type="given">Daisuke</namePart>
        <namePart type="family">Bekki</namePart>
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            <title>Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Martha</namePart>
            <namePart type="family">Palmer</namePart>
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        <name type="personal">
            <namePart type="given">Rebecca</namePart>
            <namePart type="family">Hwa</namePart>
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    <abstract>Determining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow information, capturing accurate semantics is difficult. By contrast, logical semantic representations capture deeper levels of sentence semantics, but their symbolic nature does not offer graded notions of textual similarity. We propose a method for determining semantic textual similarity by combining shallow features with features extracted from natural deduction proofs of bidirectional entailment relations between sentence pairs. For the natural deduction proofs, we use ccg2lambda, a higher-order automatic inference system, which converts Combinatory Categorial Grammar (CCG) derivation trees into semantic representations and conducts natural deduction proofs. Experiments show that our system was able to outperform other logic-based systems and that features derived from the proofs are effective for learning textual similarity.</abstract>
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    <identifier type="doi">10.18653/v1/D17-1071</identifier>
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        <date>2017-09</date>
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%0 Conference Proceedings
%T Determining Semantic Textual Similarity using Natural Deduction Proofs
%A Yanaka, Hitomi
%A Mineshima, Koji
%A Martínez-Gómez, Pascual
%A Bekki, Daisuke
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F yanaka-etal-2017-determining
%X Determining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow information, capturing accurate semantics is difficult. By contrast, logical semantic representations capture deeper levels of sentence semantics, but their symbolic nature does not offer graded notions of textual similarity. We propose a method for determining semantic textual similarity by combining shallow features with features extracted from natural deduction proofs of bidirectional entailment relations between sentence pairs. For the natural deduction proofs, we use ccg2lambda, a higher-order automatic inference system, which converts Combinatory Categorial Grammar (CCG) derivation trees into semantic representations and conducts natural deduction proofs. Experiments show that our system was able to outperform other logic-based systems and that features derived from the proofs are effective for learning textual similarity.
%R 10.18653/v1/D17-1071
%U https://aclanthology.org/D17-1071
%U https://doi.org/10.18653/v1/D17-1071
%P 681-691

Markdown (Informal)

[Determining Semantic Textual Similarity using Natural Deduction Proofs](https://aclanthology.org/D17-1071) (Yanaka et al., EMNLP 2017)

• Determining Semantic Textual Similarity using Natural Deduction Proofs (Yanaka et al., EMNLP 2017)

ACL

• Hitomi Yanaka, Koji Mineshima, Pascual Martínez-Gómez, and Daisuke Bekki. 2017. Determining Semantic Textual Similarity using Natural Deduction Proofs. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 681–691, Copenhagen, Denmark. Association for Computational Linguistics.