2023/12/05 by Reem Bin-Hezam, Mark Stevenson, Bin-Hezam, Reem +1 · 1 citation
Biochemistry, Genetics and Molecular Biology · Decision Sciences · Health Professions · #Artificial Intelligence in Healthcare #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #Data Quality and Management #FOS: Computer and information sciences #Information Retrieval (cs.IR)
paper · pdf · doi:10.48550/arxiv.2312.03171
openalex publication_date 2023/12/05 · openalex created_date 2023/12/08 · openalex updated_date 2026/07/28
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.