A scientific information extraction dataset for nature inspired engineering

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.

Original languageEnglish
Title of host publicationProceedings of the 12th Language Resources and Evaluation Conference
PublisherEuropean Language Resources Association
Pages2078-2085
Number of pages8
ISBN (Electronic)9791095546344
Publication statusPublished - May 2020
Event12th International Conference on Language Resources and Evaluation 2020 - Marseille, France
Duration: 11 May 202016 May 2020

Conference

Conference12th International Conference on Language Resources and Evaluation 2020
Abbreviated titleLREC 2020
Country/TerritoryFrance
CityMarseille
Period11/05/2016/05/20

Keywords

  • Biomimetics
  • Relation Extraction
  • Scientific Information Extraction
  • Trade-Offs

ASJC Scopus subject areas

  • Language and Linguistics
  • Education
  • Library and Information Sciences
  • Linguistics and Language

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