A Compression-Based Method for Stemmatic Analysis

Stemmatology studies relations among different variants of a text that has been gradually altered as a result of imperfectly copying the text over and over again. Underlying these variants there is what we could call a ‘family tree’, a graph representing the process of copying the text where each new version becomes a direct descendant of the exemplar(s) from which it is copied. The aim of stemmatic analysis is to reconstruct this family tree, known as the ‘stemma’, based on the surviving copies of the text. Applications are mainly in humanities, especially textual criticism, but the methods can be used to study the evolution of any symbolic objects, including chain letters [2] and computer viruses. We propose an algorithm for stemmatic analysis based on a minimum-information criterion and stochastic tree optimization. The intuitive idea behind compression-based approaches is that if a text can be significantly compressed, then the compression algorithm has found regularities which can be further exploited in an analysis such as ours. Our approach is related to phylogenetic reconstruction criteria such as maximum parsimony and maximum likelihood, and builds upon algorithmic techniques developed for bioinformatics. For a more detailed version see [14, 15].