GRLStop is a reinforcement learning stopping rule for Technology Assisted Review whose reward function lets one model serve multiple target recall levels and user-selected recall/cost tradeoffs, and it improves or matches baselines on six TAR datasets.
Using Chao's Estimator as a Stopping Criterion for Technology-Assisted Review
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
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.
fields
cs.IR 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
A Generalised and Adaptable Reinforcement Learning Stopping Method
GRLStop is a reinforcement learning stopping rule for Technology Assisted Review whose reward function lets one model serve multiple target recall levels and user-selected recall/cost tradeoffs, and it improves or matches baselines on six TAR datasets.