تقرير
Detecting Contradictory COVID-19 Drug Efficacy Claims from Biomedical Literature
العنوان: | Detecting Contradictory COVID-19 Drug Efficacy Claims from Biomedical Literature |
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المؤلفون: | Sosa, Daniel N., Suresh, Malavika, Potts, Christopher, Altman, Russ B. |
سنة النشر: | 2022 |
المجموعة: | Computer Science |
مصطلحات موضوعية: | Computer Science - Computation and Language |
الوصف: | 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 and understand the literature in this complex, high-stakes area. Our task is to automatically identify contradictory claims about COVID-19 drug efficacy. We frame this as a natural language inference problem and offer a new NLI dataset created by domain experts. The NLI framing allows us to create curricula combining existing datasets and our own. The resulting models are useful investigative tools. We provide a case study of how these models help a domain expert summarize and assess evidence concerning remdisivir and hydroxychloroquine. |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2212.09867 |
رقم الأكسشن: | edsarx.2212.09867 |
قاعدة البيانات: | arXiv |
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