# The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification

arXiv: https://arxiv.org/abs/1901.09126 · HTML: https://mlsanity.com/paper/1901.09126/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2019.

## Built on by (most cited first, 4 of 4)

- [A Survey of Active Learning for Natural Language Processing](https://mlsanity.com/paper/2210.10109/) (2022)
- [Hitting the Target: Stopping Active Learning at the Cost-Based Optimum](https://mlsanity.com/paper/2110.03802/) (2021)
- [Impact of Stop Sets on Stopping Active Learning for Text Classification](https://mlsanity.com/paper/2201.05460/) (2022)
- [Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement](https://mlsanity.com/paper/2504.16136/) (2025)

## Abstract (arXiv)

Annotation of training data is the major bottleneck in the creation of text classification systems. Active learning is a commonly used technique to reduce the amount of training data one needs to label. A crucial aspect of active learning is determining when to stop labeling data. Three potential sources for informing when to stop active learning are an additional labeled set of data, an unlabeled set of data, and the training data that is labeled during the process of active learning. To date, no one has compared and contrasted the advantages and disadvantages of stopping methods based on these three information sources. We find that stopping methods that use unlabeled data are more effective than methods that use labeled data.