This section includes seven application notes and one opinion article. The first application note by Hernandez et al. [1] presents a Twitter dataset which includes more than 120 million “potentially clinically-relevant” tweets. The tweets are automatically annotated for clinically important named entities like drugs and symptoms. The dataset is released publicly to facilitate research on mining social media data for biomedical and clinical applications. Lithgow-Serrano et al. [2] presents named entity annotation of the LitCovid [3] dataset using OntoGene’s Biomedical Entity Recogniser (OGER) [4] and shows its effectiveness for document classification. Ouyang et al. [5] presents the AGAC annotation [6] added on top of the PubTator [7] and OGER annotations and shows that the addition is potentially useful to mine regulatory or causal relationships between biomedical entities. The following three papers represent efforts for multilingualism of text mining. Barros et al. [8] presents a multilingual parallel corpus of PubMed articles for the language pairs English-Portuguese and English-Spanish. Their corpus was annotated for biomedical entities and also relationships between them, which was then used to develop a multilingual recommendation dataset for recommending biomedical entities to the authors of the articles. Yamaguchi et al. [9] and Soares et al. [10] are written by the same set of authors. They developed two versions of Japanese translation of MeSH terms, one through merging of existing resources and manual curation, and another through an automatic translation method, of which the results are reported in the two separate application notes. Larmande et al. [11] reports a revision to OryzaGP [12], a corpus of PubMed articles relevant to rice species, which are automatically annotated for proteins and genes. The last one by Dohi et al. [13] presents the authors’ opinion after their case study with Alexander disease towards visualizing the phenotype diversity.

Based on the spirit of sharing, most of the resulting datasets, including corpora, annotations, and dictionaries, are released through open repositories like GitHub, PubAnnotation/PubDictionaries [14], and so on. We hope that this special collection will be an opportunity for the readers of the journal Genomics & Informatics to get informed about recent biomedical text mining activities aimed at providing support in the current COVID-19 pandemic situation.