Information retrieval in an infodemic: the case of COVID-19 publications.

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Abstract

The coronavirus disease (COVID-19) global health crisis has led to an exponential surge in the published scientific literature. In the attempt to tackle the pandemic, extremely large COVID-19-related corpora are being created, sometimes with inaccurate information, which is no longer at scale of human analyses.In the context of searching for scientific evidence in the deluge of COVID-19-related literature, we present an information retrieval methodology for effective identification of relevant sources to answer biomedical queries posed using natural language.Our multi-stage retrieval methodology combines probabilistic weighting models and re-ranking algorithms based on deep neural architectures to boost the ranking of relevant documents. Similarity of COVID-19 queries are compared to documents and a series of post-processing methods are applied to the initial ranking list to improve the match between the query and the biomedical information source and boost the position of relevant documents.The methodology was evaluated in the context of the TREC-COVID challenge, achieving competitive results with the top-ranking teams participating in the competition. Particularly, the combination of bag-of-words and deep neural language models significantly outperformed a BM25-based baseline, retrieving on average 83% of relevant documents in the top 20.These results indicate that multi-stage retrieval supported by deep learning could enhance identification of literature for COVID-19-related questions posed using natural language.