The COVID-19 pandemic is accompanied by a massive "infodemic" that makes it hard to identify concise and credible information for COVID-19-related questions, like incubation time, infection rates, or the effectiveness of vaccines. As a novel solution, our paper is concerned with designing a question-answering system based on modern technologies from natural language processing to overcome information overload and misinformation in pandemic situations. To carry out our research, we followed a design science research approach and applied Ingwersen's cognitive model of information retrieval interaction to inform our design process from a socio-technical lens. On this basis, we derived prescriptive design knowledge in terms of design requirements and design principles, which we translated into the construction of a prototypical instantiation. Our implementation is based on the comprehensive CORD-19 dataset, and we demonstrate our artifact's usefulness by evaluating its answer quality based on a sample of COVID-19 questions labeled by biomedical experts.
翻译:COVID-19大流行伴随着一个大规模的“信息化”的“信息化”,这使得很难为COVID-19相关问题,如孵化时间、感染率或疫苗的有效性,找到简明可靠的信息。作为一个新颖的解决办法,我们的文件涉及根据自然语言处理的现代技术设计一个问答系统,以克服流行病情况下的信息超载和错误信息。为了进行研究,我们采用了设计科学研究方法,并应用Ingwersen的信息检索互动认知模型,从社会技术角度为设计过程提供信息。在此基础上,我们从设计要求和设计原则方面获取了规范性设计知识,我们将其转化为一种原型即时化。我们的实施以全面的CORD-19数据集为基础,我们通过根据生物医学专家标注的COVID-19问题样本评估其回答质量,展示了我们的艺术品的用处。