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        <title>A Scientific Information Extraction Dataset for Nature Inspired Engineering</title>
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        <namePart type="given">Ruben</namePart>
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        <namePart type="given">Julian</namePart>
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        <namePart type="family">Vincent</namePart>
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    <name type="personal">
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        <namePart type="family">Chen-Burger</namePart>
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        <namePart type="given">Marc</namePart>
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        <namePart type="family">Desmulliez</namePart>
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    <name type="personal">
        <namePart type="given">Ioannis</namePart>
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            <title>Proceedings of the Twelfth Language Resources and Evaluation Conference</title>
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    <abstract>Nature has inspired various ground-breaking technological developments in applications ranging from robotics to aerospace engineering and the manufacturing of medical devices. However, accessing the information captured in scientific biology texts is a time-consuming and hard task that requires domain-specific knowledge. Improving access for outsiders can help interdisciplinary research like Nature Inspired Engineering. This paper describes a dataset of 1,500 manually-annotated sentences that express domain-independent relations between central concepts in a scientific biology text, such as trade-offs and correlations. The arguments of these relations can be Multi Word Expressions and have been annotated with modifying phrases to form non-projective graphs. The dataset allows for training and evaluating Relation Extraction algorithms that aim for coarse-grained typing of scientific biological documents, enabling a high-level filter for engineers.</abstract>
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        <url>https://aclanthology.org/2020.lrec-1.255</url>
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        <date>2020-05</date>
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            <start>2078</start>
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%0 Conference Proceedings
%T A Scientific Information Extraction Dataset for Nature Inspired Engineering
%A Kruiper, Ruben
%A Vincent, Julian F.V.
%A Chen-Burger, Jessica
%A Desmulliez, Marc P.Y.
%A Konstas, Ioannis
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F kruiper-etal-2020-scientific
%X Nature has inspired various ground-breaking technological developments in applications ranging from robotics to aerospace engineering and the manufacturing of medical devices. However, accessing the information captured in scientific biology texts is a time-consuming and hard task that requires domain-specific knowledge. Improving access for outsiders can help interdisciplinary research like Nature Inspired Engineering. This paper describes a dataset of 1,500 manually-annotated sentences that express domain-independent relations between central concepts in a scientific biology text, such as trade-offs and correlations. The arguments of these relations can be Multi Word Expressions and have been annotated with modifying phrases to form non-projective graphs. The dataset allows for training and evaluating Relation Extraction algorithms that aim for coarse-grained typing of scientific biological documents, enabling a high-level filter for engineers.
%U https://aclanthology.org/2020.lrec-1.255
%P 2078-2085

Markdown (Informal)

[A Scientific Information Extraction Dataset for Nature Inspired Engineering](https://aclanthology.org/2020.lrec-1.255) (Kruiper et al., LREC 2020)

• A Scientific Information Extraction Dataset for Nature Inspired Engineering (Kruiper et al., LREC 2020)

ACL

• Ruben Kruiper, Julian F.V. Vincent, Jessica Chen-Burger, Marc P.Y. Desmulliez, and Ioannis Konstas. 2020. A Scientific Information Extraction Dataset for Nature Inspired Engineering. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 2078–2085, Marseille, France. European Language Resources Association.