High-precision Biomedical Text Corpora for Multi-Entity Recognition: A CoDiet study

Lain, A. D.; Go, S.; Mahmud, A.; Rajendra, S.; Cano San Jose, A.; Loupasaki, K.; Theodoridis, G.; Bizkarguenaga Uribiarte, M.; Gu, Y.; Deda, O.; Conde, R. D. A.; Embade, N.; de Diego Rodriguez, A.; Burguera, N.; Rossiou, D.; Gil Redondo, R.; Gallou, D.; Tueros, I.; Velmurugan, R.; Gkanali, V.; Caro Burgos, M.; Pousinis, P.; Alektoridis, G.; Arranz, S.; Nikolopoulos, N.; Yan, X.; Fernandez Carrion, R.; Rowlands, T.; Choi, D.; Rei, M.; Cave-Ayland, C.; D Alessandro, A.; The CoDiet consortium,; Beck, T.; Posma, J. M.

doi:10.1101/2025.09.04.673740

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bioRxiv · 10.1101/2025.09.04.673740

High-precision Biomedical Text Corpora for Multi-Entity Recognition: A CoDiet study

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Abstract

We present here five biomedical, multi-entity corpora that can be used as benchmarks for named-entity recognition (NER), targeted to literature on metabolic syndrome. The CoDiet-Gold corpus contains annotations for 500 full-text publications and 348,406 annotations. It is divided into CoDiet-Gold-public (450 documents) and CoDiet-Gold-private (50 documents). Each document was independently annotated by two human experts, with disagreements fully adjudicated by a third expert. The CoDiet-Electrum corpus (2,998,273 annotations) contains 4,423 publications that were annotated using case-insensitive matching of the surface forms with punctuation ignored, found in CoDiet-Gold-public. Finally, for the same 4,423 documents, two fully machine annotated corpora CoDiet-Bronze (2,938,738 annotations) and CoDiet-Silver (2,298,988 annotations), were created by utilising existing NER algorithms to annotate these. These corpora contain categories (organisms, disease, genes, proteins, metabolites) that add depth to existing corpora, as well as new categories that do not appear in other corpora (food, dietary methods, sample types, computational methods, study methodology, population characteristics, data types, and microbiome).

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Lain, A. D., Go, S., Mahmud, A., Rajendra, S., Cano San Jose, A., Loupasaki, K., Theodoridis, G., Bizkarguenaga Uribiarte, M., Gu, Y., Deda, O., Conde, R. D. A., Embade, N., de Diego Rodriguez, A., Burguera, N., Rossiou, D., Gil Redondo, R., Gallou, D., Tueros, I., Velmurugan, R., Gkanali, V., Caro Burgos, M., Pousinis, P., Alektoridis, G., Arranz, S., Nikolopoulos, N., Yan, X., Fernandez Carrion, R., Rowlands, T., Choi, D., Rei, M., Cave-Ayland, C., D Alessandro, A., The CoDiet consortium,, Beck, T., Posma, J. M.. 2025-09-09. High-precision Biomedical Text Corpora for Multi-Entity Recognition: A CoDiet study. https://doi.org/10.1101/2025.09.04.673740

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