Few-shot learning for medical text: A systematic review

Y Ge, Y Guo, YC Yang, MA Al-Garadi… - arXiv preprint arXiv …, 2022 - arxiv.org
Objective: Few-shot learning (FSL) methods require small numbers of labeled instances for
training. As many medical topics have limited annotated textual data in practical settings …

Predicting demands of COVID-19 prevention and control materials via co-evolutionary transfer learning

Q Song, YJ Zheng, J Yang, YJ Huang… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
The novel coronavirus pneumonia (COVID-19) has created great demands for medical
resources. Determining these demands timely and accurately is critically important for the …

Measuring geographic performance disparities of offensive language classifiers

B Lwowski, P Rad, A Rios - arXiv preprint arXiv:2209.07353, 2022 - arxiv.org
Text classifiers are applied at scale in the form of one-size-fits-all solutions. Nevertheless,
many studies show that classifiers are biased regarding different languages and dialects …

COVID-19-related Nepali tweets classification in a low resource setting

R Adhikari, S Thapaliya, N Basnet, S Poudel… - arXiv preprint arXiv …, 2022 - arxiv.org
Billions of people across the globe have been using social media platforms in their local
languages to voice their opinions about the various topics related to the COVID-19 …

When Infodemic Meets Epidemic: a Systematic Literature Review

C Asaad, I Khaouja, M Ghogho, K Baïna - arXiv preprint arXiv:2210.04612, 2022 - arxiv.org
Epidemics and outbreaks present arduous challenges requiring both individual and
communal efforts. Social media offer significant amounts of data that can be leveraged for …

[PDF][PDF] Few-shot learning for medical text: A systematic

Y Ge, Y Guo, YC Yang, MA Al-Garadi, A Sarker - researchgate.net
Objective Few-shot learning (FSL) methods require small numbers of labeled instances for
training. As many medical topics have limited annotated textual data in practical settings …