Logion: Machine Learning for Greek Philology

2023/05/01 by Charlie Cowen-Breen, Creston Brooks, Cowen-Breen, Charlie +5
Arts and Humanities · Computer Science · #Computation and Language (cs.CL) #Digital Humanities and Scholarship #FOS: Computer and information sciences #Handwritten Text Recognition Techniques #I.2.7 #Machine Learning (cs.LG) #Natural Language Processing Techniques

paper · pdf · doi:10.48550/arxiv.2305.01099

openalex publication_date 2023/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper presents machine-learning methods to address various problems in Greek philology. After training a BERT model on the largest premodern Greek dataset used for this purpose to date, we identify and correct previously undetected errors made by scribes in the process of textual transmission, in what is, to our knowledge, the first successful identification of such errors via machine learning. Additionally, we demonstrate the model's capacity to fill gaps caused by material deterioration of premodern manuscripts and compare the model's performance to that of a domain expert. We find that best performance is achieved when the domain expert is provided with model suggestions for inspiration. With such human-computer collaborations in mind, we explore the model's interpretability and find that certain attention heads appear to encode select grammatical features of premodern Greek.

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