重新利用TREC-COVID注释来回答CORD-19的关键问题
Repurposing TREC-COVID Annotations to Answer the Key Questions of CORD-19
摘要：
2019年新型冠状病毒病（COVID-19）于2019年底在中国武汉开始，迄今已感染全球超过1400万人，导致超过75万人死亡。2020年3月10日，世界卫生组织宣布疫情为全球大流行。许多学者和研究人员，不限于医学领域，开始发表论文描述新的发现。然而，随着大量出版物的涌入，这些人很难筛选大量的数据并理解这些发现。白宫和由艾伦人工智能研究所领导的一组行业研究实验室汇总了20多万篇与各种冠状病毒相关的期刊文章，并要求社区回答与语料库相关的关键问题，将数据集作为CORD-19发布。信息检索（IR）社区重新利用CORD-19中的期刊文章，使其更接近于经典的TREC式竞赛，称为TREC-COVID，人类注释者在每轮竞赛结束时提供相关性判断。看到相关的努力，我们开始重新调整TREC-COVID任务的相关性注释，以识别CORD-19中与CORD-19提出的关键问题相关的期刊文章。在这个重新设计的数据集上训练的BioBERT模型为CORD-19任务规定了相关性注释，这些任务在科恩的kappa方面与大多数人类注释的总体一致率为0.4430。我们提出了用于构建新数据集的方法，并描述了整个决策过程。
英文摘要：
The novel coronavirus disease 2019 (COVID-19) began in Wuhan, China in late 2019 and to date has infected over 14M people worldwide, resulting in over 750,000 deaths. On March 10, 2020 the World Health Organization (WHO) declared the outbreak a global pandemic. Many academics and researchers, not restricted to the medical domain, began publishing papers describing new discoveries. However, with the large influx of publications, it was hard for these individuals to sift through the large amount of data and make sense of the findings. The White House and a group of industry research labs, lead by the Allen Institute for AI, aggregated over 200,000 journal articles related to a variety of coronaviruses and tasked the community with answering key questions related to the corpus, releasing the dataset as CORD-19. The information retrieval (IR) community repurposed the journal articles within CORD-19 to more closely resemble a classic TREC-style competition, dubbed TREC-COVID, with human annotators providing relevancy judgements at the end of each round of competition. Seeing the related endeavors, we set out to repurpose the relevancy annotations for TREC-COVID tasks to identify journal articles in CORD-19 which are relevant to the key questions posed by CORD-19. A BioBERT model trained on this repurposed dataset prescribes relevancy annotations for CORD-19 tasks that have an overall agreement of 0.4430 with majority human annotations in terms of Cohen's kappa. We present the methodology used to construct the new dataset and describe the decision process used throughout.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
