Contextual Unsupervised Clustering of Signs for Ancient Writing Systems

The application of machine learning techniques to ancient writing systems is a relatively new idea, and it poses interesting challenges for researchers. One particularly challenging aspect is the scarcity of data for these scripts, which contrasts with the large amounts of data usually available when applying neural models to computational linguistics and other fields. For this reason, any method that attempts to work on ancient scripts needs to be ad-hoc and consider paleographic aspects, in addition to computational ones. Considering the peculiar characteristics of the script that we used is therefore be a crucial part of our work, as any solution needs to consider the particular nature of the writing system that it is applied to. In this work we propose a preliminary evaluation of a novel unsupervised clustering method on Cypro-Greek syllabary, a writing system from Cyprus. This evaluation shows that our method improves clustering performance using information about the attested sequences of signs in combination with an unsupervised model for images, with the future goal of applying the methodology to undeciphered writing systems from a related and typologically similar script.