Computational methods applied to undeciphered scripts from the Aegean and Cyprus: Linear A and Cypro-Minoan

Corazza, Michele (2023) Computational methods applied to undeciphered scripts from the Aegean and Cyprus: Linear A and Cypro-Minoan, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Culture letterarie e filologiche, 35 Ciclo. DOI 10.48676/unibo/amsdottorato/10975.
Documenti full-text disponibili:
[img] Documento PDF (English) - Richiede un lettore di PDF come Xpdf o Adobe Acrobat Reader
Disponibile con Licenza: Salvo eventuali più ampie autorizzazioni dell'autore, la tesi può essere liberamente consultata e può essere effettuato il salvataggio e la stampa di una copia per fini strettamente personali di studio, di ricerca e di insegnamento, con espresso divieto di qualunque utilizzo direttamente o indirettamente commerciale. Ogni altro diritto sul materiale è riservato.
Download (5MB)


The study of ancient, undeciphered scripts presents unique challenges, that depend both on the nature of the problem and on the peculiarities of each writing system. In this thesis, I present two computational approaches that are tailored to two different tasks and writing systems. The first of these methods is aimed at the decipherment of the Linear A afraction signs, in order to discover their numerical values. This is achieved with a combination of constraint programming, ad-hoc metrics and paleographic considerations. The second main contribution of this thesis regards the creation of an unsupervised deep learning model which uses drawings of signs from ancient writing system to learn to distinguish different graphemes in the vector space. This system, which is based on techniques used in the field of computer vision, is adapted to the study of ancient writing systems by incorporating information about sequences in the model, mirroring what is often done in natural language processing. In order to develop this model, the Cypriot Greek Syllabary is used as a target, since this is a deciphered writing system. Finally, this unsupervised model is adapted to the undeciphered Cypro-Minoan and it is used to answer open questions about this script. In particular, by reconstructing multiple allographs that are not agreed upon by paleographers, it supports the idea that Cypro-Minoan is a single script and not a collection of three script like it was proposed in the literature. These results on two different tasks shows that computational methods can be applied to undeciphered scripts, despite the relatively low amount of available data, paving the way for further advancement in paleography using these methods.

Tipologia del documento
Tesi di dottorato
Corazza, Michele
Dottorato di ricerca
Settore disciplinare
Settore concorsuale
Parole chiave
Computational Paleography, Deep Learning, Natural Language Processing, Computational Linguistics, Unsupervised Learning
Data di discussione
15 Giugno 2023

Altri metadati

Statistica sui download

Gestione del documento: Visualizza la tesi