# NatCat: Weakly Supervised Text Classification with Naturally Annotated Resources

arXiv: https://arxiv.org/abs/2009.14335 · HTML: https://mlsanity.com/paper/2009.14335/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

## Built on by (most cited first, 3 of 3)

- [WRENCH: A Comprehensive Benchmark for Weak Supervision](https://mlsanity.com/paper/2109.11377/) (2021)
- [The Benefits of Label-Description Training for Zero-Shot Text Classification](https://mlsanity.com/paper/2305.02239/) (2023)
- [Towards Open-Domain Topic Classification](https://mlsanity.com/paper/2306.17290/) (2023)

## Abstract (arXiv)

We describe NatCat, a large-scale resource for text classification constructed from three data sources: Wikipedia, Stack Exchange, and Reddit. NatCat consists of document-category pairs derived from manual curation that occurs naturally within online communities. To demonstrate its usefulness, we build general purpose text classifiers by training on NatCat and evaluate them on a suite of 11 text classification tasks (CatEval), reporting large improvements compared to prior work. We benchmark different modeling choices and resource combinations and show how tasks benefit from particular NatCat data sources.