A Scientific Information Extraction Dataset for Nature Inspired Engineering

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

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.
Anthology ID:
2020.lrec-1.255
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
2078–2085
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.255
DOI:
Bibkey:
Cite (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.
Cite (Informal):
A Scientific Information Extraction Dataset for Nature Inspired Engineering (Kruiper et al., LREC 2020)
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PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/2020.lrec-1.255.pdf
Code
 rubenkruiper/FOBIE
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