Restoration of fragmentary Babylonian texts using recurrent neural networks.
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ABSTRACT:
SUBMITTER: Fetaya E
PROVIDER: S-EPMC7502733 | biostudies-literature | 2020 Sep
REPOSITORIES: biostudies-literature
Publications
Restoration of fragmentary Babylonian texts using recurrent neural networks.
Fetaya Ethan E Lifshitz Yonatan Y Aaron Elad E Gordin Shai S
Proceedings of the National Academy of Sciences of the United States of America 20200901 37
The main sources of information regarding ancient Mesopotamian history and culture are clay cuneiform tablets. Many of these tablets are damaged, leading to missing information. Currently, the missing text is manually reconstructed by experts. We investigate the possibility of assisting scholars, by modeling the language using recurrent neural networks and automatically completing the breaks in ancient Akkadian texts from Achaemenid period Babylonia. ...[more]
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