This article explores an unconventional method for classifying text by leveraging compression algorithms. The author demonstrates how to concatenate labeled documents, compress them, and use the compressed sizes to predict labels for new texts. While the method shows promise, it is computationally expensive and generally underperforms compared to traditional classifiers.

This article explores how Python 3.14's zstd module enables efficient text classification through incremental compression. It outlines a method where text is classified based on the size of compressed output from different class-specific compressors, demonstrating improved speed and accuracy over traditional methods.