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http://dx.doi.org/10.5808/gi.21015

OryzaGP 2021 update: a rice gene and protein dataset for named-entity recognition  

Larmande, Pierre (DIADE, Univ. Montpellier, IRD, CIRAD)
Liu, Yusha (Hubei Provincial Key Laboratory of Agricultural Bioinformatics, College of informatics, Huazhong Agricultural University)
Yao, Xinzhi (Hubei Provincial Key Laboratory of Agricultural Bioinformatics, College of informatics, Huazhong Agricultural University)
Xia, Jingbo (Hubei Provincial Key Laboratory of Agricultural Bioinformatics, College of informatics, Huazhong Agricultural University)
Publication Information
Genomics & Informatics / v.19, no.3, 2021 , pp. 27.1-27.4 More about this Journal
Abstract
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 pretrained NLP models. Finally, we analysed the annotation results and discussed how to improve OryzaGP.
Keywords
biological dataset; gene mention; named entity recognition; natural language processing; Oryza species;
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