论文标题
RMITB在TREC COVID 2020
RMITB at TREC COVID 2020
论文作者
论文摘要
搜索引擎用户很少使用相同的查询表达信息需求，而查询的小差异可能会导致非常不同的结果集。这些用户查询的变化已在过去的TREC核心轨道上被利用，以在离线评估活动中贡献多样化，高效的运行，目的是生产可重复使用的测试集。在本文中，我们使用第一作者的每个主题进行了十个查询，记录了提交到TREC COVID的第一轮和第二轮的查询融合运行。在我们的分析中，我们主要关注从判断池中省略第二个优先级的影响。此运行特别令人感兴趣，因为它浮出水面，直到任务后期才判断这些文件。如果在第一轮中包括了其他判断，则使用RBP P = 0.5时，此次运行的性能提高了35个职位，强调了判断力深度和覆盖范围在评估任务中的重要性。
Search engine users rarely express an information need using the same query, and small differences in queries can lead to very different result sets. These user query variations have been exploited in past TREC CORE tracks to contribute diverse, highly-effective runs in offline evaluation campaigns with the goal of producing reusable test collections. In this paper, we document the query fusion runs submitted to the first and second round of TREC COVID, using ten queries per topic created by the first author. In our analysis, we focus primarily on the effects of having our second priority run omitted from the judgment pool. This run is of particular interest, as it surfaced a number of relevant documents that were not judged until later rounds of the task. If the additional judgments were included in the first round, the performance of this run increased by 35 rank positions when using RBP p=0.5, highlighting the importance of judgment depth and coverage in assessment tasks.
