论文标题
从协议到筛选：用于技术辅助系统文献评论的混合学习方法
From Protocol to Screening: A Hybrid Learning Approach for Technology-Assisted Systematic Literature Reviews
论文作者
论文摘要
在医学领域中，系统文献综述（SLR）试图收集适合预指定资格标准的所有经验证据，以回答一个特定的研究问题。准备SLR的过程包括多个劳动密集型且耗时的任务，涉及巨大的货币成本。技术辅助评论（TAR）方法可以使创建SLR的不同过程自动化，它们尤其专注于减轻审查员筛查的负担。我们提出了一种用于焦油的新方法，该方法实现了从研究方案到筛选相关论文的完整管道。我们的管道克服了由专家构建的布尔查询的需求，由三个不同的组件组成：主要检索引擎，跨浏览等级器和内部评估等级，将学习对秩技术与相关反馈方法相结合。此外，我们为CLEF 2019 EHEALTH LAB DATASET的任务2的更新版本提供了更新的版本，我们可以公开使用。该数据集的经验结果表明，我们的方法可以实现最先进的结果。
In the medical domain, a Systematic Literature Review (SLR) attempts to collect all empirical evidence, that fit pre-specified eligibility criteria, in order to answer a specific research question. The process of preparing an SLR consists of multiple tasks that are labor-intensive and time-consuming, involving large monetary costs. Technology-assisted review (TAR) methods automate the different processes of creating an SLR and they are particularly focused on reducing the burden of screening for reviewers. We present a novel method for TAR that implements a full pipeline from the research protocol to the screening of the relevant papers. Our pipeline overcomes the need of a Boolean query constructed by specialists and consists of three different components: the primary retrieval engine, the inter-review ranker and the intra-review ranker, combining learning-to-rank techniques with a relevance feedback method. In addition, we contribute an updated version of the Task 2 of the CLEF 2019 eHealth Lab dataset, which we make publicly available. Empirical results on this dataset show that our approach can achieve state-of-the-art results.
