# Improving Probabilistic Models in Text Classification via Active Learning

arXiv: https://arxiv.org/abs/2202.02629 · HTML: https://mlsanity.com/paper/2202.02629/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 69 references that are in the map.
- First posted on arXiv 5 Feb 2022.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach](https://mlsanity.com/paper/2010.07835/) (2020)
- [On Statistical Bias In Active Learning: How and When To Fix It](https://mlsanity.com/paper/2101.11665/) (2021)
- [Active Learning: Problem Settings and Recent Developments](https://mlsanity.com/paper/2012.04225/) (2020)
- [Stopping criterion for active learning based on deterministic generalization bounds](https://mlsanity.com/paper/2005.07402/) (2020)
- [Keyword Assisted Topic Models](https://mlsanity.com/paper/2004.05964/) (2020)
- [Stopping Active Learning based on Predicted Change of F Measure for Text Classification](https://mlsanity.com/paper/1901.09118/) (2019)

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

- [When Predictions Become Regressors: A Split-Sample Correction for Biases in Downstream Inference](https://mlsanity.com/paper/2608.02909/) (2026)

## Abstract (arXiv)

Social scientists often classify text documents to use the resulting labels as an outcome or a predictor in empirical research. Automated text classification has become a standard tool, since it requires less human coding. However, scholars still need many human-labeled documents to train automated classifiers. To reduce labeling costs, we propose a new algorithm for text classification that combines a probabilistic model with active learning. The probabilistic model uses both labeled and unlabeled data, and active learning concentrates labeling efforts on difficult documents to classify. Our validation study shows that the classification performance of our algorithm is comparable to state-of-the-art methods at a fraction of the computational cost. Moreover, we replicate two recently published articles and reach the same substantive conclusions with only a small proportion of the original labeled data used in those studies. We provide activeText, an open-source software to implement our method.