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:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.lrec-1.255.pdf
- Code
- rubenkruiper/FOBIE