A Scientific Information Extraction Dataset for Nature Inspired Engineering
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.
Cite
@article{arxiv.2005.07753,
title = {A Scientific Information Extraction Dataset for Nature Inspired Engineering},
author = {Ruben Kruiper and Julian F. V. Vincent and Jessica Chen-Burger and Marc P. Y. Desmulliez and Ioannis Konstas},
journal= {arXiv preprint arXiv:2005.07753},
year = {2020}
}
Comments
Published in Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020). Updated dataset statistics, results unchanged