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Awane Widad1, Ben Lahmar El Habib1, El Falaki Ayoub1

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This study develops a deep learning question answering system using COVID-19 research articles. The system provides precise answers to enhance pandemic information dissemination and public awareness.

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Area of Science:

  • Artificial Intelligence
  • Biomedical Informatics
  • Public Health

Background:

  • The COVID-19 pandemic generated a surge in scholarly publications, necessitating reliable information access.
  • A critical need exists for accurate information to guide public awareness and preventive actions against the pandemic.
  • Existing information retrieval systems may struggle with the volume and specificity of COVID-19 research.

Purpose of the Study:

  • To develop a high-precision deep learning system for answering questions about COVID-19.
  • To leverage open-source scientific literature for building a robust question-answering (QA) model.
  • To improve the accessibility and reliability of COVID-19 information for researchers and the public.

Main Methods:

  • Utilized open-source scientific and academic articles related to COVID-19.
  • Implemented a question answering system based on the BERT model.
  • Fine-tuned the BERT model on the Stanford Question Answering Dataset (SQuAD) benchmark.
  • Selected relevant documents to train and evaluate the QA system.

Main Results:

  • The developed deep learning system demonstrates high precision in answering subject-specific questions.
  • The BERT-based QA system effectively processes and extracts information from COVID-19 literature.
  • Fine-tuning on SQuAD improved the model's ability to understand and respond to complex queries.

Conclusions:

  • The proposed deep learning QA system offers a reliable method for accessing precise information within the COVID-19 research corpus.
  • This approach can significantly aid in disseminating accurate information, supporting public health initiatives.
  • The system's effectiveness highlights the potential of AI in managing and utilizing large-scale scientific data during health crises.