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Restoration of fragmentary Babylonian texts using recurrent neural networks
Ethan Fetaya, Yonatan Lifshitz, Elad Aaron et al., 2020.
What it's used for here
PNAS 117(37). Recurrent neural network language models of Achaemenid-period Babylonian (Akkadian) archival texts, used to propose fillings for broken passages; first machine-learning restoration work on cuneiform. Used for the fragment-restoration line of work.
Cited by on this site 4 records
EVT
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The unread cuneiform: half a million tablets and the limits of Sumerian
MYSThe mystery article
Cite it
Ethan Fetaya, Yonatan Lifshitz, Elad Aaron, & Shai Gordin (2020). Restoration of fragmentary Babylonian texts using recurrent neural networks. https://doi.org/10.1073/pnas.2003794117
@article{fetaya2020restoration,
author = {Ethan Fetaya} and {Yonatan Lifshitz} and {Elad Aaron} and {Shai Gordin},
title = {Restoration of fragmentary Babylonian texts using recurrent neural networks},
year = {2020},
doi = {10.1073/pnas.2003794117},
url = {https://doi.org/10.1073/pnas.2003794117}
}TY - JOUR TI - Restoration of fragmentary Babylonian texts using recurrent neural networks AU - Ethan Fetaya AU - Yonatan Lifshitz AU - Elad Aaron AU - Shai Gordin PY - 2020 DO - 10.1073/pnas.2003794117 UR - https://doi.org/10.1073/pnas.2003794117 ER -
et al.. “Restoration of fragmentary Babylonian texts using recurrent neural networks.” 2020. https://doi.org/10.1073/pnas.2003794117.