# Text Ranking and Classification using Data Compression

arXiv: https://arxiv.org/abs/2109.11577 · HTML: https://mlsanity.com/paper/2109.11577/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 23 Sep 2021.

## Builds on (most cited first, 1 of 1)

- [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://mlsanity.com/paper/1705.02364/) (2017)

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

- [Less is More: Parameter-Free Text Classification with Gzip](https://mlsanity.com/paper/2212.09410/) (2022)
- [Lightweight Conceptual Dictionary Learning for Text Classification Using Information Compression](https://mlsanity.com/paper/2405.01584/) (2024)
- [An Introduction to Compression-Based Machine Learning](https://mlsanity.com/paper/2609.21309/) (2026)

## Abstract (arXiv)

A well-known but rarely used approach to text categorization uses conditional entropy estimates computed using data compression tools. Text affinity scores derived from compressed sizes can be used for classification and ranking tasks, but their success depends on the compression tools used. We use the Zstandard compressor and strengthen these ideas in several ways, calling the resulting language-agnostic technique Zest. In applications, this approach simplifies configuration, avoiding careful feature extraction and large ML models. Our ablation studies confirm the value of individual enhancements we introduce. We show that Zest complements and can compete with language-specific multidimensional content embeddings in production, but cannot outperform other counting methods on public datasets.