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    <titleInfo>
        <title>Graphene: Semantically-Linked Propositions in Open Information Extraction</title>
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    <name type="personal">
        <namePart type="given">Matthias</namePart>
        <namePart type="family">Cetto</namePart>
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    <name type="personal">
        <namePart type="given">Christina</namePart>
        <namePart type="family">Niklaus</namePart>
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    <name type="personal">
        <namePart type="given">André</namePart>
        <namePart type="family">Freitas</namePart>
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    <name type="personal">
        <namePart type="given">Siegfried</namePart>
        <namePart type="family">Handschuh</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
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            <namePart type="family">Bender</namePart>
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            <namePart type="given">Leon</namePart>
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            <namePart type="given">Pierre</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>We present an Open Information Extraction (IE) approach that uses a two-layered transformation stage consisting of a clausal disembedding layer and a phrasal disembedding layer, together with rhetorical relation identification. In that way, we convert sentences that present a complex linguistic structure into simplified, syntactically sound sentences, from which we can extract propositions that are represented in a two-layered hierarchy in the form of core relational tuples and accompanying contextual information which are semantically linked via rhetorical relations. In a comparative evaluation, we demonstrate that our reference implementation Graphene outperforms state-of-the-art Open IE systems in the construction of correct n-ary predicate-argument structures. Moreover, we show that existing Open IE approaches can benefit from the transformation process of our framework.</abstract>
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        <url>https://aclanthology.org/C18-1195</url>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T Graphene: Semantically-Linked Propositions in Open Information Extraction
%A Cetto, Matthias
%A Niklaus, Christina
%A Freitas, André
%A Handschuh, Siegfried
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F cetto-etal-2018-graphene
%X We present an Open Information Extraction (IE) approach that uses a two-layered transformation stage consisting of a clausal disembedding layer and a phrasal disembedding layer, together with rhetorical relation identification. In that way, we convert sentences that present a complex linguistic structure into simplified, syntactically sound sentences, from which we can extract propositions that are represented in a two-layered hierarchy in the form of core relational tuples and accompanying contextual information which are semantically linked via rhetorical relations. In a comparative evaluation, we demonstrate that our reference implementation Graphene outperforms state-of-the-art Open IE systems in the construction of correct n-ary predicate-argument structures. Moreover, we show that existing Open IE approaches can benefit from the transformation process of our framework.
%U https://aclanthology.org/C18-1195
%P 2300-2311

Markdown (Informal)

[Graphene: Semantically-Linked Propositions in Open Information Extraction](https://aclanthology.org/C18-1195) (Cetto et al., COLING 2018)

• Graphene: Semantically-Linked Propositions in Open Information Extraction (Cetto et al., COLING 2018)

ACL

• Matthias Cetto, Christina Niklaus, André Freitas, and Siegfried Handschuh. 2018. Graphene: Semantically-Linked Propositions in Open Information Extraction. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2300–2311, Santa Fe, New Mexico, USA. Association for Computational Linguistics.