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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2505.06974 |
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| _version_ | 1866915282903105536 |
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| author | Kohmoto, Daichi Fukuda, Katsutoshi Yoshida, Daisuke Matsui, Takafumi Omura, Sachihiro |
| author_facet | Kohmoto, Daichi Fukuda, Katsutoshi Yoshida, Daisuke Matsui, Takafumi Omura, Sachihiro |
| contents | A cuneiform tablet KBo 23.1 ++/KUB 30.38, which is known to represent a text of Kizzuwatna rituals, was written by two writers with almost identical content in two iterations. Unlike other cuneiform tablets that contained information such as myths, essays, or business records, the reason why ancient people left such tablets for posterity remains unclear. To study this problem, we develop a new methodology by analyzing images of a tablet quantitatively using CNN (Convolutional Neural Network)-based image models, without segmenting cuneiforms one-by-one. Our data-driven methodology implies that the writer writing the first half was a `teacher' and the other writer was a `student' who was training his skills of writing cuneiforms. This result has not been reached by classical linguistics. We also discuss related conclusions and possible further directions for applying our method and its generalizations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06974 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CNN-based Image Models Verify a Hypothesis that The Writers of Cuneiform Texts Improved Their Writing Skills When Studying at the Age of Hittite Empire Kohmoto, Daichi Fukuda, Katsutoshi Yoshida, Daisuke Matsui, Takafumi Omura, Sachihiro Computation and Language A cuneiform tablet KBo 23.1 ++/KUB 30.38, which is known to represent a text of Kizzuwatna rituals, was written by two writers with almost identical content in two iterations. Unlike other cuneiform tablets that contained information such as myths, essays, or business records, the reason why ancient people left such tablets for posterity remains unclear. To study this problem, we develop a new methodology by analyzing images of a tablet quantitatively using CNN (Convolutional Neural Network)-based image models, without segmenting cuneiforms one-by-one. Our data-driven methodology implies that the writer writing the first half was a `teacher' and the other writer was a `student' who was training his skills of writing cuneiforms. This result has not been reached by classical linguistics. We also discuss related conclusions and possible further directions for applying our method and its generalizations. |
| title | CNN-based Image Models Verify a Hypothesis that The Writers of Cuneiform Texts Improved Their Writing Skills When Studying at the Age of Hittite Empire |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.06974 |