Restoration of fragmentary Babylonian texts using recurrent neural networks

Restoration of fragmentary Babylonian texts using recurrent neural networks

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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Proceedings of the National Academy of Sciences - PNAS 2020-09, Vol.117 (37), p.22743-22751
Hauptverfasser: Fetaya, Ethan, Lifshitz, Yonatan, Aaron, Elad, Gordin, Shai
Format: Artikel
Sprache:eng
Schlagworte:
Multidisciplinary Sciences
Neural networks
Physical Sciences
Recurrent neural networks
Science & Technology
Science & Technology - Other Topics
Social Sciences
Tablets
Texts
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.2003794117