Detecting contradictory COVID-19 drug efficacy claims from biomedical literature. (2023)
Presentation / Conference Contribution
SOSA, D.N., SURESH, M., POTTS, C. and ALTMAN, R.B. 2023. Detecting contradictory COVID-19 drug efficacy claims from biomedical literature. In Rogers, A., Boyd-Graber, J. and Okazaki, N. (eds.) Proceedings of the 61st Association for Computational Linguistics annual meeting 2023 (ACL 2023), 9-14 July 2023, Toronto, Candada. Stroudsburg, PA: ACL [online], volume 2: short papers, pages 694-713. Available from: https://doi.org/10.18653/v1/2023.acl-short.61

The COVID-19 pandemic created a deluge of questionable and contradictory scientific claims about drug efficacy – an "infodemic" with lasting consequences for science and society. In this work, we argue that NLP models can help domain experts distill... Read More about Detecting contradictory COVID-19 drug efficacy claims from biomedical literature..