Using large language models to extract plant functional traits from unstructured text
The natural language processing pipeline we propose has the potential to facilitate the digitization and extraction of large amounts of plant functional trait information residing in scattered textual descriptions.
Authors
- Viktor Domazetoski (ORCID: https://orcid.org/0000-0001-9830-7032)
- Helena Bestová (ORCID: https://orcid.org/0000-0003-2589-7195)
- Patrick Weigelt (ORCID: https://orcid.org/0000-0002-2485-3708)
- Philipp Wieder (ORCID: https://orcid.org/0000-0002-6992-1866)
- Alireza Zarei
- Holger Kreft (ORCID: https://orcid.org/0000-0003-4471-8236)
- Radoslav Koynov
Institutions
- Radboud University Nijmegen (NL)
- Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen (DE)
- University of Göttingen (DE)
Publication Details
- Journal
- Applications in Plant Sciences
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1002/aps3.70011
- Citations
- 4
- Primary Topic
- Species Distribution and Climate Change
- Type
- article
- Field-Weighted Citation Impact
- 7.35
Funders
- Deutsche Forschungsgemeinschaft