Restoration of fragmentary Babylonian texts using recurrent neural networks

Ethan Fetaya; Yonatan Lifshitz; Elad Aaron; Shai Gordin

doi:10.1073/pnas.2003794117

Restoration of fragmentary Babylonian texts using recurrent neural networks

Ethan Fetaya
, Yonatan Lifshitz
, Elad Aaron
, Shai Gordin

Department of Land of Israel Studies and Archaeology

نتاج البحث: نشر في مجلة › مقالة › مراجعة النظراء

48 اقتباسات (Scopus)

ملخص

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.

اللغة الأصلية	الإنجليزيّة
الصفحات (من إلى)	22743-22751
عدد الصفحات	9
دورية	Proceedings of the National Academy of Sciences of the United States of America
مستوى الصوت	117
رقم الإصدار	37
المعرِّفات الرقمية للأشياء	https://doi.org/10.1073/pnas.2003794117 https://doi.org/10.1073/pnas.2003794117
حالة النشر	نُشِر - 15 سبتمبر 2020

الوصول إلى المستند

الملفات والروابط الأخرى

Link to publication in Scopus

قم بذكر هذا

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title = "Restoration of fragmentary Babylonian texts using recurrent neural networks",

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.",

keywords = "Babylonian heritage, cuneiform script, Late Babylonian dialect, Achaemenid empire, neural networks",

author = "Ethan Fetaya and Yonatan Lifshitz and Elad Aaron and Shai Gordin",

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doi = "10.1073/pnas.2003794117",

language = "אנגלית",

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Restoration of fragmentary Babylonian texts using recurrent neural networks. / Fetaya, Ethan; Lifshitz, Yonatan; Aaron, Elad وآخرون.
في: Proceedings of the National Academy of Sciences of the United States of America, المجلد 117, رقم 37, 15.09.2020, صفحة 22743-22751.

نتاج البحث: نشر في مجلة › مقالة › مراجعة النظراء

TY - JOUR

T1 - Restoration of fragmentary Babylonian texts using recurrent neural networks

AU - Fetaya, Ethan

AU - Lifshitz, Yonatan

AU - Aaron, Elad

AU - Gordin, Shai

PY - 2020/9/15

Y1 - 2020/9/15

N2 - 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.

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KW - Babylonian heritage

KW - cuneiform script

KW - Late Babylonian dialect

KW - Achaemenid empire

KW - neural networks

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DO - 10.1073/pnas.2003794117

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JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

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Restoration of fragmentary Babylonian texts using recurrent neural networks

ملخص

الوصول إلى المستند

الملفات والروابط الأخرى

بصمة

قم بذكر هذا