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Literature DB >> 32873650

Restoration of fragmentary Babylonian texts using recurrent neural networks.

Ethan Fetaya¹, Yonatan Lifshitz², Elad Aaron³, Shai Gordin⁴.

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.

Entities: Disease

Keywords: Achaemenid empire; Babylonian heritage; Late Babylonian dialect; cuneiform script; neural networks

Year: 2020 PMID： 32873650 PMCID： PMC7502733 DOI： 10.1073/pnas.2003794117

Source DB: PubMed Journal: Proc Natl Acad Sci U S A ISSN： 0027-8424 Impact factor: 11.205

3 in total

Restoration of fragmentary Babylonian texts using recurrent neural networks.

1. A Markov model of the Indus script.

2. Long short-term memory.

3. Statistical analysis of the Indus script using n-grams.

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2. Restoring and attributing ancient texts using deep neural networks.

3. A Computational Neural Network Model for College English Grammar Correction.