Bin-Hezam, R. and Stevenson, R. orcid.org/0000-0002-9483-6006 (2023) Combining counting processes and classification improves a stopping rule for technology assisted review. In: Bouamor, H., Pino, J. and Bali, K., (eds.) Findings of the Association for Computational Linguistics: EMNLP 2023. The 2023 Conference on Empirical Methods in Natural Language Processing, 06-10 Dec 2023, Singapore. Association for Computational Linguistics , pp. 2603-2609.
Abstract
Technology Assisted Review (TAR) stopping rules aim to reduce the cost of manually assessing documents for relevance by minimising the number of documents that need to be examined to ensure a desired level of recall. This paper extends an effective stopping rule using information derived from a text classifier that can be trained without the need for any additional annotation. Experiments on multiple data sets (CLEF e-Health, TREC Total Recall, TREC Legal and RCV1) showed that the proposed approach consistently improves performance and outperforms several alternative methods.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 Association for Computational Linguistics (ACL). This work is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Oct 2023 11:22 |
Last Modified: | 08 Dec 2023 16:40 |
Published Version: | https://aclanthology.org/2023.findings-emnlp.171 |
Status: | Published |
Publisher: | Association for Computational Linguistics |
Refereed: | Yes |
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Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:204479 |