Technology Assisted Review (TAR) stopping rules aim to reduce the cost of manually assessing documents for relevance by minimising the number of documents that need to be examined to ensure a desired level of recall. This paper extends an effective stopping rule using information derived from a text classifier that can be trained without the need for any additional annotation. Experiments on multiple data sets (CLEF e-Health, TREC Total Recall, TREC Legal and RCV1) showed that the proposed approach consistently improves performance and outperforms several alternative methods.

翻译：技术辅助审查（TAR）停止规则旨在通过最小化确保达到预期召回率所需审查的文档数量，降低人工评估文档相关性的成本。本文利用从文本分类器中获取的信息（该分类器无需额外标注即可训练）扩展了一种有效的停止规则。在多个数据集（CLEF e-Health、TREC Total Recall、TREC Legal 和 RCV1）上的实验表明，所提出的方法能够持续提升性能，并优于多种替代方法。