Automated Question Answering System for FAQ COVID-19 Using Word Embeddings

This study's scope includes the development of a Question Answering System for COVID-19 and a review of that system. This is unlike previous work in biomedical QAS, which primarily targets technical users. This work leans towards developing a COVID-19 QAS customized for the general public, especially those who have limited knowledge in the clinical field. This study aims to present the development and analysis of COVID-19 QAS systems. The methodology was based on the development of the system, which included conducting experiments to check the accuracy of the QAS system. Consequently, the QAS system would process the user query using three different feature extraction approaches and output the related FAQ and the answer associated with it from a set of 561 FAQs that were sourced from the Ministry of Health, the Virginia Department of Health, and the World Health Organization. The accuracy of the ensuing responses has been tested by Qaviar. The experimental results indicated that BERT achieved the highest accuracy across all datasets consistently, with 96.25%–98%; Word2Vec scored 86.25%–95.2%, while Bow scored between 86.24% and 88%. While most models performed stably, the performance of Word2Vec was comparatively unstable across data sets. Generally, the lowest accuracy value resulted for all models on the smallest dataset. Increased size of the datasets might not necessarily result in higher accuracy. Generally, BERT outperformed the other embedding approaches.

@article{Elfadil2025,
  title     = {Automated Question Answering System for FAQ COVID-19 Using Word Embeddings},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Nazar Elfadil and Sarah Saad Alanazi},
  doi       = {10.14569/IJACSA.2025.0161277},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161277}
}