# Know thy corpus! Robust methods for digital curation of Web corpora

arXiv: https://arxiv.org/abs/2003.06389 · HTML: https://mlsanity.com/paper/2003.06389/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2020.

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

- [Impact of Pretraining Term Frequencies on Few-Shot Reasoning](https://mlsanity.com/paper/2202.07206/) (2022)
- [MAUVE Scores for Generative Models: Theory and Practice](https://mlsanity.com/paper/2212.14578/) (2022)
- [ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review](https://mlsanity.com/paper/2305.03123/) (2023)
- [Register Always Matters: Analysis of LLM Pretraining Data Through the Lens of Language Variation](https://mlsanity.com/paper/2504.01542/) (2025)
- [The Curious Case of Control](https://mlsanity.com/paper/2205.12113/) (2022)
- [Autoencoder-Based Framework to Capture Vocabulary Quality in NLP](https://mlsanity.com/paper/2503.00209/) (2025)

## Abstract (arXiv)

This paper proposes a novel framework for digital curation of Web corpora in order to provide robust estimation of their parameters, such as their composition and the lexicon. In recent years language models pre-trained on large corpora emerged as clear winners in numerous NLP tasks, but no proper analysis of the corpora which led to their success has been conducted. The paper presents a procedure for robust frequency estimation, which helps in establishing the core lexicon for a given corpus, as well as a procedure for estimating the corpus composition via unsupervised topic models and via supervised genre classification of Web pages. The results of the digital curation study applied to several Web-derived corpora demonstrate their considerable differences. First, this concerns different frequency bursts which impact the core lexicon obtained from each corpus. Second, this concerns the kinds of texts they contain. For example, OpenWebText contains considerably more topical news and political argumentation in comparison to ukWac or Wikipedia. The tools and the results of analysis have been released.