论文标题
从小事物大事增长：一个带有种子研究的收藏，用于医学系统评论文献搜索
From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search
论文作者
论文摘要
医学系统审查查询配方是由训练有素的信息专家完成的一项高度复杂的任务。复杂性来自对冗长的布尔查询的依赖，该查询表达了一个详细的研究问题。为了帮助查询配方，信息专家在查询制定之前使用一组称为“种子研究”的示例文档。种子研究有助于验证查询的有效性，并在全面评估检索到的研究之前。除了种子的使用之外，特定的IR方法还可以利用种子研究来指导自动查询配方和新的检索模型。迄今为止，工作的一个主要局限性是这些方法通过回顾性使用纳入的研究（即相关性评估）来利用“伪种子研究”。但是，我们显示伪种子研究并不代表信息专家使用的实际种子研究。因此，我们提供了一个测试收集，该测试收集使用用于协助查询制定的现实种子研究。为了支持我们的收集，我们提供了以前不可能的分析种子研究如何影响检索并使用基于种子研究的方法进行多个实验，以比较使用种子研究与伪种子研究的有效性。我们在http://github.com/ielab/sysrev-seed-collection上提供了测试收集以及所有实验和分析的结果
Medical systematic review query formulation is a highly complex task done by trained information specialists. Complexity comes from the reliance on lengthy Boolean queries, which express a detailed research question. To aid query formulation, information specialists use a set of exemplar documents, called `seed studies', prior to query formulation. Seed studies help verify the effectiveness of a query prior to the full assessment of retrieved studies. Beyond this use of seeds, specific IR methods can exploit seed studies for guiding both automatic query formulation and new retrieval models. One major limitation of work to date is that these methods exploit `pseudo seed studies' through retrospective use of included studies (i.e., relevance assessments). However, we show pseudo seed studies are not representative of real seed studies used by information specialists. Hence, we provide a test collection with real world seed studies used to assist with the formulation of queries. To support our collection, we provide an analysis, previously not possible, on how seed studies impact retrieval and perform several experiments using seed-study based methods to compare the effectiveness of using seed studies versus pseudo seed studies. We make our test collection and the results of all of our experiments and analysis available at http://github.com/ielab/sysrev-seed-collection
