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

Year: 2023

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2312.03171

Authors: Bin-Hezam, Reem, Stevenson, Mark

Institutions: Princess Nourah bint Abdulrahman University, University of Sheffield

Language: English

Topic: Information Retrieval and Search Behavior

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4389471000

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