Reading Akkadian cuneiform using natural language processing

In this paper we present a new method for automatic transliteration and segmentation of Unicode cuneiform glyphs using Natural Language Processing (NLP) techniques. Cuneiform is one of the earliest known writing system in the world, which documents millennia of human civilizations in the ancient Nea...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2020-10, Vol.15 (10), p.e0240511-e0240511
Main Authors: Gordin, Shai, Gutherz, Gai, Elazary, Ariel, Romach, Avital, Jiménez, Enrique, Berant, Jonathan, Cohen, Yoram
Format: Article
Language:English
Subjects:
Akkadian language
Algorithms
Applications programs
Archaeology
Biology and Life Sciences
Civilization
Computer and Information Sciences
Computer based research
Cuneiform
History, Ancient
Humanities
Humans
Inscriptions
Language
Language - history
Learning algorithms
Machine learning
Man-machine interfaces
Metadata
Middle East
Museums
Natural Language Processing
Neural networks
Phonetics
Physical sciences
Reading
Recurrent neural networks
Research and Analysis Methods
Segmentation
Signs
Social Sciences
Software
Texts
Translations and translating
Transliteration
Writing systems
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper we present a new method for automatic transliteration and segmentation of Unicode cuneiform glyphs using Natural Language Processing (NLP) techniques. Cuneiform is one of the earliest known writing system in the world, which documents millennia of human civilizations in the ancient Near East. Hundreds of thousands of cuneiform texts were found in the nineteenth and twentieth centuries CE, most of which are written in Akkadian. However, there are still tens of thousands of texts to be published. We use models based on machine learning algorithms such as recurrent neural networks (RNN) with an accuracy reaching up to 97% for automatically transliterating and segmenting standard Unicode cuneiform glyphs into words. Therefore, our method and results form a major step towards creating a human-machine interface for creating digitized editions. Our code, Akkademia, is made publicly available for use via a web application, a python package, and a github repository.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0240511