S1000: a better taxonomic name corpus for biomedical information extraction
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Motivation: The recognition of mentions of species names in text is a critically important task for biomedical text mining. While deep learningbased
methods have made great advances in many named entity recognition tasks, results for species name recognition remain poor. We hypothesize
that this is primarily due to the lack of appropriate corpora.
Results: We introduce the S1000 corpus, a comprehensive manual re-annotation and extension of the S800 corpus. We demonstrate that
S1000 makes highly accurate recognition of species names possible (F-score¼93.1%), both for deep learning and dictionary-based methods.
Availability and implementation: All resources introduced in this study are available under open licenses from https://jensenlab.org/resources/
s1000/. The webpage contains links to a Zenodo project and three GitHub repositories associated with the study.
methods have made great advances in many named entity recognition tasks, results for species name recognition remain poor. We hypothesize
that this is primarily due to the lack of appropriate corpora.
Results: We introduce the S1000 corpus, a comprehensive manual re-annotation and extension of the S800 corpus. We demonstrate that
S1000 makes highly accurate recognition of species names possible (F-score¼93.1%), both for deep learning and dictionary-based methods.
Availability and implementation: All resources introduced in this study are available under open licenses from https://jensenlab.org/resources/
s1000/. The webpage contains links to a Zenodo project and three GitHub repositories associated with the study.
Originalsprog | Engelsk |
---|---|
Artikelnummer | btad369 |
Tidsskrift | Bioinformatics |
Vol/bind | 39 |
Udgave nummer | 6 |
Antal sider | 8 |
ISSN | 1367-4803 |
DOI | |
Status | Udgivet - 2023 |
Bibliografisk note
Funding Information:
This work was supported by Novo Nordisk Foundation [grant number NNF14CC0001]; and by the Academy of Finland [grant number 332844]. K.N. has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie [grant number 101023676].
Publisher Copyright:
© 2023 The Author(s).
ID: 360982850