High Recall Retrieval Via Technology-Assisted Review

被引:0
|
作者
Gray, Lenora [1 ]
Lewis, David D. [1 ]
Pickens, Jeremy [1 ]
Yang, Eugene [2 ]
机构
[1] Redgrave Data, Chantilly, VA 20151 USA
[2] Johns Hopkins Univ, HLTCOE, Baltimore, MD USA
来源
PROCEEDINGS OF THE 47TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, SIGIR 2024 | 2024年
关键词
text classification; human-in-the-loop; active learning; generative; AI; statistical evaluation;
D O I
10.1145/3626772.3661376
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
High Recall Retrieval (HRR) tasks, including eDiscovery in the law, systematic literature reviews, and sunshine law requests focus on efficiently prioritizing relevant documents for human review. Technology-assisted review (TAR) refers to iterative human-in-theloop workflows that combine human review with IR and AI techniques to minimize both time and manual effort while maximizing recall. This full-day tutorial provides a comprehensive introduction to TAR. The morning session presents an overview of the key technologies and workflow designs used, the basics of practical evaluation methods, and the social and ethical implications of TAR deployment. The afternoon session provides more technical depth on the implications of TAR workflows for supervised learning algorithm design, how generative AI is can be applied in TAR, more sophisticated statistical evaluation techniques, and a wide range of open research questions.
引用
收藏
页码:2987 / 2988
页数:2
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