Reading Akkadian cuneiform using natural language processing

被引:10
|
作者
Gordin, Shai [1 ]
Gutherz, Gai [2 ]
Elazary, Ariel [2 ]
Romach, Avital [3 ]
Jimenez, Enrique [4 ]
Berant, Jonathan [2 ]
Cohen, Yoram [3 ]
机构
[1] Ariel Univ, Digital Humanities Ariel Lab, Fac Social Sci & Humanities, Ariel, Israel
[2] Tel Aviv Univ, Sch Comp Sci, Tel Aviv, Israel
[3] Tel Aviv Univ, Jacob M Alkow Dept Archaeol & Ancient Near Easter, Tel Aviv, Israel
[4] Ludwig Maximilians Univ Munchen, Inst Assyriol & Hittitol, Munich, Germany
来源
PLOS ONE | 2020年 / 15卷 / 10期
关键词
D O I
10.1371/journal.pone.0240511
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
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页数:16
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