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arXiv · 2005.07753

A Scientific Information Extraction Dataset for Nature Inspired Engineering

Ruben Kruiper·Julian F. V. Vincent·Jessica Chen-Burger·Marc P. Y. Desmulliez·Ioannis Konstas
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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.

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Ruben Kruiper, Julian F. V. Vincent, Jessica Chen-Burger, Marc P. Y. Desmulliez, Ioannis Konstas. 2020-05-15. A Scientific Information Extraction Dataset for Nature Inspired Engineering. https://arxiv.org/abs/2005.07753

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