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.

• Compression-based classification (concatenate labeled texts, measure compressed-size increase) hits a 0.749 macro F1 with gzip on 4 categories of 20 Newsgroups, versus 0.88 for multinomial Naive Bayes.
• lzma pushes accuracy up to 0.897, beating Naive Bayes, but takes 32 minutes versus over 5 minutes for gzip on just 1,353 test cases—far too slow to be practical.
• The technique reframes text classification as an information-theory problem, showing compression algorithms implicitly model word probability distributions.