Document detail
ID
oai:arXiv.org:2405.02525
Topic
Computer Science - Information Ret...Author
Bin-Hezam, Reem Stevenson, MarkCategory
Computer Science
Year
2024
listing date
6/12/2024
Keywords
tarAbstract
We present RLStop, a novel Technology Assisted Review (TAR) stopping rule based on reinforcement learning that helps minimise the number of documents that need to be manually reviewed within TAR applications.
RLStop is trained on example rankings using a reward function to identify the optimal point to stop examining documents.
Experiments at a range of target recall levels on multiple benchmark datasets (CLEF e-Health, TREC Total Recall, and Reuters RCV1) demonstrated that RLStop substantially reduces the workload required to screen a document collection for relevance.
RLStop outperforms a wide range of alternative approaches, achieving performance close to the maximum possible for the task under some circumstances.
;Comment: Accepted at SIGIR 2024
Bin-Hezam, Reem,Stevenson, Mark, 2024, RLStop: A Reinforcement Learning Stopping Method for TAR
