Restoration of fragmentary Babylonian texts using recurrent neural networks.
basic_science · Level V
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- Record sourced from PubMed, PMID 32873650.
- Also identified by DOI 10.1073/pnas.2003794117 and PMC identifier 7502733.
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Abstract
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.