RLStop: A Reinforcement Learning Stopping Method for TAR-Reference-Cited by-同舟云学术

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RLStop: A Reinforcement Learning Stopping Method for TAR

Published:2024-07-10 Issue: Volume:1866 Page:2604-2608
ISSN:
Container-title:Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Bin-Hezam Reem¹^ORCID,Stevenson Mark²^ORCID

Affiliation:

1. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University & Department of Computer Science, Faculty of Engineering, University of Sheffield, Riyadh, Saudi Arabia

2. Department of Computer Science, Faculty of Engineering, University of Sheffield, Sheffield, United Kingdom

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626772.3657911

Reference42 articles.

1. Measure-based metasearch

2. Jason R Baron Mahmoud F Sayed and Douglas W Oard. 2020. Providing More Efficient Access To Government Records: A Use Case Involving Application of Machine Learning to Improve FOIA Review for the Deliberative Process Privilege. arXiv preprint arXiv:2011.07203 (2020).

3. Combining Counting Processes and Classification Improves a Stopping Rule for Technology Assisted Review

4. Statistical stopping criteria for automated screening in systematic reviews

5. Gordon Cormack and Maura Grossman. 2015. Autonomy and Reliability of Continuous Active Learning for Technology-Assisted Review. arXiv preprint arXiv:1504.06868 (apr 2015). arxiv: 1504.06868

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