Journal article
Restoring ancient text using deep learning: a case study on Greek epigraphy
- Abstract:
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Ancient History relies on disciplines such as Epigraphy, the study of ancient inscribed texts, for evidence of the recorded past. However, these texts, “inscriptions”, are often damaged over the centuries, and illegible parts of the text must be restored by specialists, known as epigraphists. This work presents Pythia, the first ancient text restoration model that recovers missing characters from a damaged text input using deep neural networks. Its architecture is carefully designed to handle...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Bibliographic Details
- Publisher:
- Association for Computational Linguistics Publisher's website
- Journal:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) Journal website
- Pages:
- 6369–6376
- Publication date:
- 2019-11-07
- Acceptance date:
- 2019-11-01
- DOI:
Item Description
- Keywords:
- Pubs id:
-
pubs:1063713
- UUID:
-
uuid:6b344f53-8bcf-40bb-91ca-2e1a08f89a88
- Local pid:
- pubs:1063713
- Source identifiers:
-
1063713
- Deposit date:
- 2019-11-21
Terms of use
- Copyright holder:
- Association for Computational Linguistics
- Copyright date:
- 2019
- Notes:
- © 2019 Association for Computational Linguistics. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License. This is a conference paper presented at the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, November 3–7, Hong Kong, China.
- Licence:
- CC Attribution (CC BY)
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