# Language-agnostic Topic Classification for Wikipedia

arXiv: https://arxiv.org/abs/2103.00068 · HTML: https://mlsanity.com/paper/2103.00068/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2021.

## Built on by (most cited first, 6 of 6)

- [Boosted Dense Retriever](https://mlsanity.com/paper/2112.07771/) (2021)
- [Talk to Right Specialists: Iterative Routing in Multi-agent Systems for Question Answering](https://mlsanity.com/paper/2501.07813/) (2025)
- [Contextual Categorization Enhancement through LLMs Latent-Space](https://mlsanity.com/paper/2404.16442/) (2024)
- [Considerations for Multilingual Wikipedia Research](https://mlsanity.com/paper/2204.02483/) (2022)
- [Hoaxpedia: A Unified Wikipedia Hoax Articles Dataset](https://mlsanity.com/paper/2405.02175/) (2024)
- [Language-Agnostic Modeling of Source Reliability on Wikipedia](https://mlsanity.com/paper/2410.18803/) (2024)

## Abstract (arXiv)

A major challenge for many analyses of Wikipedia dynamics -- e.g., imbalances in content quality, geographic differences in what content is popular, what types of articles attract more editor discussion -- is grouping the very diverse range of Wikipedia articles into coherent, consistent topics. This problem has been addressed using various approaches based on Wikipedia's category network, WikiProjects, and external taxonomies. However, these approaches have always been limited in their coverage: typically, only a small subset of articles can be classified, or the method cannot be applied across (the more than 300) languages on Wikipedia. In this paper, we propose a language-agnostic approach based on the links in an article for classifying articles into a taxonomy of topics that can be easily applied to (almost) any language and article on Wikipedia. We show that it matches the performance of a language-dependent approach while being simpler and having much greater coverage.