Larmande Pierre auteur aut IRD Liu Y. auteur aut IRD Yao X. auteur aut IRD Xia J. auteur aut IRD text journalArticle eng text/pdf reformatted digital access Due to the rapid evolution of high-throughput technologies, a tremendous amount of data is being produced in the biological domain, which poses a challenging task for information extraction and natural language understanding. Biological named entity recognition (NER) and named entity normalisation (NEN) are two common tasks aiming at identifying and linking biologically important entities such as genes or gene products mentioned in the literature to biological databases. In this paper, we present an updated version of OryzaGP, a gene and protein dataset for rice species created to help natural language processing (NLP) tools in processing NER and NEN tasks. To create the dataset, we selected more than 15,000 abstracts associated with articles previously curated for rice genes. We developed four dictionaries of gene and protein names associated with database identifiers. We used these dictionaries to annotate the dataset. We also annotated the dataset using pre-trained NLP models. Finally, we analysed the annotation results and discussed how to improve OryzaGP. specialized 020 122 19 3 e27 [4 ] 2021 2234-0742 https://www.documentation.ird.fr/hor/fdi:010093865 10.5808/gi.21015 2234-0742 [F B010093865] https://www.documentation.ird.fr/hor/fdi:010093865 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2025-07/010093865.pdf IRD - Base Horizon / Pleins textes 2025-06-02 2025-07-09 fdi:010093865 fre