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 language | English |
|---|---|
| Title of host publication | Proceedings of the 12th Language Resources and Evaluation Conference |
| Publisher | European Language Resources Association |
| Pages | 2078-2085 |
| Number of pages | 8 |
| ISBN (Electronic) | 9791095546344 |
| Publication status | Published - May 2020 |
| Event | 12th International Conference on Language Resources and Evaluation 2020 - Marseille, France Duration: 11 May 2020 → 16 May 2020 https://lrec2020.lrec-conf.org/en/index.html |
Conference
| Conference | 12th International Conference on Language Resources and Evaluation 2020 |
|---|---|
| Abbreviated title | LREC 2020 |
| Country/Territory | France |
| City | Marseille |
| Period | 11/05/20 → 16/05/20 |
| Internet address |
Keywords
- Biomimetics
- Relation Extraction
- Scientific Information Extraction
- Trade-Offs
ASJC Scopus subject areas
- Language and Linguistics
- Education
- Library and Information Sciences
- Linguistics and Language