  We introduce new dataset 'CORD-19-Vaccination' to cater to scientists
specifically looking into COVID-19 vaccine-related research. This dataset is
extracted from CORD-19 dataset [Wang et al., 2020] and augmented with new
columns for language detail, author demography, keywords, and topic per paper.
Facebook's fastText model is used to identify languages [Joulin et al., 2016].
To establish author demography (author affiliation, lab/institution location,
and lab/institution country columns) we processed the JSON file for each paper
and then further enhanced using Google's search API to determine country
values. 'Yake' was used to extract keywords from the title, abstract, and body
of each paper and the LDA (Latent Dirichlet Allocation) algorithm was used to
add topic information [Campos et al., 2020, 2018a,b]. To evaluate the dataset,
we demonstrate a question-answering task like the one used in the CORD-19
Kaggle challenge [Goldbloom et al., 2022]. For further evaluation, sequential
sentence classification was performed on each paper's abstract using the model
from Dernoncourt et al. [2016]. We partially hand annotated the training
dataset and used a pre-trained BERT-PubMed layer. 'CORD- 19-Vaccination'
contains 30k research papers and can be immensely valuable for NLP research
such as text mining, information extraction, and question answering, specific
to the domain of COVID-19 vaccine research.
