This study uses a novel simulation framework to evaluate whether the time and effort necessary to achieve high recall using active learning is reduced by presenting the reviewer with isolated sentences, as opposed to full documents, for relevance feedback. Under the weak assumption that more time and effort is required to review an entire document than a single sentence, simulation results indicate that the use of isolated sentences for relevance feedback can yield comparable accuracy and higher efficiency, relative to the state-of-the-art baseline model implementation (BMI) of the AutoTAR continuous active learning (“CAL”) method employed in the TREC 2015 and 2016 Total Recall Track.

中文翻译：

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评估句子级别的相关性反馈以获取高召回率的信息

这项研究使用一种新颖的模拟框架来评估是否通过向审阅者呈现孤立的句子（而不是完整的文档）以减少相关反馈，从而减少了使用主动学习来实现高召回率所需的时间和精力。在一个较弱的假设下，与一个句子相比，审阅整个文档需要花费更多的时间和精力，模拟结果表明，相对于当前状态，将独立的句子用于相关性反馈可以产生相当的准确性和更高的效率。 TREC 2015和2016 Total Recall Track中采用的AutoTAR连续主动学习（“ CAL”）方法的艺术基准模型实施（BMI）。