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Bibliographic Details
Main Authors: Bin-Hezam, Reem, Stevenson, Mark
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2405.02525
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author Bin-Hezam, Reem
Stevenson, Mark
author_facet Bin-Hezam, Reem
Stevenson, Mark
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2405_02525
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle RLStop: A Reinforcement Learning Stopping Method for TAR
Bin-Hezam, Reem
Stevenson, Mark
Information Retrieval
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.
title RLStop: A Reinforcement Learning Stopping Method for TAR
topic Information Retrieval
url https://arxiv.org/abs/2405.02525