JMIR Publications

ABSTRACT

Background:

The 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.

Objective:

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.

Methods:

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.

Results:

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.

Conclusions:

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.