@inproceedings{cowen-breen-etal-2023-logion,
title = "Logion: Machine-Learning Based Detection and Correction of Textual Errors in {G}reek Philology",
author = "Cowen-Breen, Charlie and
Brooks, Creston and
Graziosi, Barbara and
Haubold, Johannes",
editor = "Anderson, Adam and
Gordin, Shai and
Li, Bin and
Liu, Yudong and
Passarotti, Marco C.",
booktitle = "Proceedings of the Ancient Language Processing Workshop",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.alp-1.20",
pages = "170--178",
abstract = "We present statistical and machine-learning based techniques for detecting and correcting errors in text and apply them to the challenge of textual corruption in Greek philology. Most ancient Greek texts reach us through a long process of copying, in relay, from earlier manuscripts (now lost). In this process of textual transmission, copying errors tend to accrue. 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. The premodern Greek BERT model we train is available for use at https://huggingface.co/cabrooks/LOGION-base.",
}
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<abstract>We present statistical and machine-learning based techniques for detecting and correcting errors in text and apply them to the challenge of textual corruption in Greek philology. Most ancient Greek texts reach us through a long process of copying, in relay, from earlier manuscripts (now lost). In this process of textual transmission, copying errors tend to accrue. 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. The premodern Greek BERT model we train is available for use at https://huggingface.co/cabrooks/LOGION-base.</abstract>
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%0 Conference Proceedings
%T Logion: Machine-Learning Based Detection and Correction of Textual Errors in Greek Philology
%A Cowen-Breen, Charlie
%A Brooks, Creston
%A Graziosi, Barbara
%A Haubold, Johannes
%Y Anderson, Adam
%Y Gordin, Shai
%Y Li, Bin
%Y Liu, Yudong
%Y Passarotti, Marco C.
%S Proceedings of the Ancient Language Processing Workshop
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F cowen-breen-etal-2023-logion
%X We present statistical and machine-learning based techniques for detecting and correcting errors in text and apply them to the challenge of textual corruption in Greek philology. Most ancient Greek texts reach us through a long process of copying, in relay, from earlier manuscripts (now lost). In this process of textual transmission, copying errors tend to accrue. 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. The premodern Greek BERT model we train is available for use at https://huggingface.co/cabrooks/LOGION-base.
%U https://aclanthology.org/2023.alp-1.20
%P 170-178
Markdown (Informal)
[Logion: Machine-Learning Based Detection and Correction of Textual Errors in Greek Philology](https://aclanthology.org/2023.alp-1.20) (Cowen-Breen et al., ALP-WS 2023)
ACL