Attention-based bidirectional LSTM with embedding technique for classification of COVID-19 articles

The epidemic of COVID-19 has thrown the planet into an awfully tricky situation putting a terrifying end to thousands of lives; the global health infrastructure continues to be in significant danger. Several machine learning techniques and pre-defined models have been demonstrated to accomplish the classification of COVID-19 articles. These delineate strategies to extract information from structured and unstructured data sources which form the article repository for physicians and researchers. Expanding the knowledge of diagnosis and treatment of COVID-19 virus is the key benefit of these researches. A multi-label Deep Learning classification model has been proposed here on the LitCovid dataset which is a collection of research articles on coronavirus. Relevant prior articles are explored to select appropriate network parameters that could promote the achievement of a stable Artificial Neural Network mechanism for COVID-19 virus-related challenges. We have noticed that the proposed classification model achieves accuracy and micro-F1 score of 75.95% and 85.2, respectively. The experimental result also indicates that the propound technique outperforms the surviving methods like BioBERT and Longformer.