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Automated approaches to identifying authorship of a text have become commonplace in the stylometric studies. The current article applies an unsupervised stylometric approach on Middle English documents using the script Stylo in R, in an attempt to distinguish between texts from different dialectal areas. The approach is based on the distribution of character 3-grams generated from the texts of the corpus of Middle English Local Documents (MELD). The article adopts the middle ground in the study of Middle English spelling variation, between the concept of relational linguistic space and the real linguistic continuum of medieval England. Stylo can distinguish between Middle English dialects by using the less frequent character 3-grams.

The advent of ever-larger and more diverse historical corpora for different historical periods and linguistic varieties has led to the impossibility of obtaining simple, direct-and yet balancedrepresentations of the core patterns in the data. In order to draw insights from heterogeneous and complex materials of this type, historical linguists have begun to reach for a growing number of data visualisation techniques, from the statistical, to the cartographical, the network-based and beyond. An exploration of the state of this art was the objective of a workshop at the 2018 International Conference on English Historical Linguistics, from whence most of the materials of this Special Issue are drawn. This brief introductory paper outlines the background and relevance of this line of methodological research and presents a summary of the individual papers that make up the collection.