@inproceedings{zadworny-gordin-2025-assignment,
title = "Assignment of account type to proto-cuneiform economic texts with Multi-Class Support Vector Machines",
author = "Zadworny, Piotr and
Gordin, Shai",
editor = "Anderson, Adam and
Gordin, Shai and
Li, Bin and
Liu, Yudong and
Passarotti, Marco C. and
Sprugnoli, Rachele",
booktitle = "Proceedings of the Second Workshop on Ancient Language Processing",
month = may,
year = "2025",
address = "The Albuquerque Convention Center, Laguna",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.alp-1.3/",
doi = "10.18653/v1/2025.alp-1.3",
pages = "22--30",
ISBN = "979-8-89176-235-0",
abstract = "We investigate the use of machine learning for classifying proto-cuneiform economic texts (3,500-3,000 BCE), leveraging Multi-Class Support Vector Machines (MSVM) to assign text type based on content. Proto-cuneiform presents unique challenges, as it does not en-code spoken language, yet is transcribed into linear formats that obscure original structural elements. We address this by reformatting tran-scriptions, experimenting with different tok-enization strategies, and optimizing feature ex-traction. Our workflow achieves high label-ing reliability and enables significant metadata enrichment. In addition to improving digital corpus organization, our approach opens the chance to identify economic institutions in an-cient Mesopotamian archives, providing a new tool for Assyriological research."
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<abstract>We investigate the use of machine learning for classifying proto-cuneiform economic texts (3,500-3,000 BCE), leveraging Multi-Class Support Vector Machines (MSVM) to assign text type based on content. Proto-cuneiform presents unique challenges, as it does not en-code spoken language, yet is transcribed into linear formats that obscure original structural elements. We address this by reformatting tran-scriptions, experimenting with different tok-enization strategies, and optimizing feature ex-traction. Our workflow achieves high label-ing reliability and enables significant metadata enrichment. In addition to improving digital corpus organization, our approach opens the chance to identify economic institutions in an-cient Mesopotamian archives, providing a new tool for Assyriological research.</abstract>
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%0 Conference Proceedings
%T Assignment of account type to proto-cuneiform economic texts with Multi-Class Support Vector Machines
%A Zadworny, Piotr
%A Gordin, Shai
%Y Anderson, Adam
%Y Gordin, Shai
%Y Li, Bin
%Y Liu, Yudong
%Y Passarotti, Marco C.
%Y Sprugnoli, Rachele
%S Proceedings of the Second Workshop on Ancient Language Processing
%D 2025
%8 May
%I Association for Computational Linguistics
%C The Albuquerque Convention Center, Laguna
%@ 979-8-89176-235-0
%F zadworny-gordin-2025-assignment
%X We investigate the use of machine learning for classifying proto-cuneiform economic texts (3,500-3,000 BCE), leveraging Multi-Class Support Vector Machines (MSVM) to assign text type based on content. Proto-cuneiform presents unique challenges, as it does not en-code spoken language, yet is transcribed into linear formats that obscure original structural elements. We address this by reformatting tran-scriptions, experimenting with different tok-enization strategies, and optimizing feature ex-traction. Our workflow achieves high label-ing reliability and enables significant metadata enrichment. In addition to improving digital corpus organization, our approach opens the chance to identify economic institutions in an-cient Mesopotamian archives, providing a new tool for Assyriological research.
%R 10.18653/v1/2025.alp-1.3
%U https://aclanthology.org/2025.alp-1.3/
%U https://doi.org/10.18653/v1/2025.alp-1.3
%P 22-30
Markdown (Informal)
[Assignment of account type to proto-cuneiform economic texts with Multi-Class Support Vector Machines](https://aclanthology.org/2025.alp-1.3/) (Zadworny & Gordin, ALP 2025)
ACL