  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.
