Technology-Assisted Review (TAR) aims to reduce the human effort required for screening processes such as abstract screening for systematic literature reviews. Human reviewers label documents as relevant or irrelevant during this process, while the system incrementally updates a prediction model based on the reviewers' previous decisions. After each model update, the system proposes new documents it deems relevant, to prioritize relevant documentsover irrelevant ones. A stopping criterion is necessary to guide users in stopping the review process to minimize the number of missed relevant documents and the number of read irrelevant documents. In this paper, we propose and evaluate a new ensemble-based Active Learning strategy and a stopping criterion based on Chao's Population Size Estimator that estimates the prevalence of relevant documents in the dataset. Our simulation study demonstrates that this criterion performs well on several datasets and is compared to other methods presented in the literature.

翻译：技术辅助评审（Technology-Assisted Review, TAR）旨在减少筛查过程中所需的人力投入，例如系统性文献综述中的摘要筛查。在此过程中，人工评审员将文档标注为相关或不相关，而系统则根据评审员先前的决策增量式更新预测模型。每次模型更新后，系统会提出其认为相关的新文档，以便优先处理相关文档而非无关文档。为引导用户停止评审过程、最小化遗漏相关文档数量以及减少阅读无关文档数量，需要设定一个停止准则。本文提出并评估了一种基于集成学习的主动学习新策略，以及基于Chao总体规模估计量的停止准则——该估计量用于估算数据集中相关文档的普遍性。我们的模拟研究表明，该准则在多个数据集上表现良好，并与文献中提出的其他方法进行了对比分析。