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

arXiv: https://arxiv.org/abs/2312.03171 · HTML: https://mlsanity.com/paper/2312.03171/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 5 Dec 2023.

## Builds on (most cited first, 2 of 2)

- [Autonomy and Reliability of Continuous Active Learning for Technology-Assisted Review](https://mlsanity.com/paper/1504.06868/) (2015)
- [Continuous Active Learning Using Pretrained Transformers](https://mlsanity.com/paper/2208.06955/) (2022)

## Built on by (most cited first, 1 of 1)

- [The Next Phase of Scientific Fact-Checking: Advanced Evidence Retrieval from Complex Structured Academic Papers](https://mlsanity.com/paper/2506.20844/) (2025)

## Abstract (arXiv)

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.