Exhaustive Entity Recognition for Coptic: Challenges and Solutions

Amir Zeldes, Lance Martin, Sichang Tu


Abstract
Entity recognition provides semantic access to ancient materials in the Digital Humanities: it exposes people and places of interest in texts that cannot be read exhaustively, facilitates linking resources and can provide a window into text contents, even for texts with no translations. In this paper we present entity recognition for Coptic, the language of Hellenistic era Egypt. We evaluate NLP approaches to the task and lay out difficulties in applying them to a low-resource, morphologically complex language. We present solutions for named and non-named nested entity recognition and semi-automatic entity linking to Wikipedia, relying on robust dependency parsing, feature-based CRF models, and hand-crafted knowledge base resources, enabling high accuracy NER with orders of magnitude less data than those used for high resource languages. The results suggest avenues for research on other languages in similar settings.
Anthology ID:
2020.latechclfl-1.3
Volume:
Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
Month:
December
Year:
2020
Address:
Online
Venues:
CLFL | COLING | LaTeCH | LaTeCHCLfL
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Publisher:
International Committee on Computational Linguistics
Note:
Pages:
19–28
Language:
URL:
https://aclanthology.org/2020.latechclfl-1.3
DOI:
Bibkey:
Cite (ACL):
Amir Zeldes, Lance Martin, and Sichang Tu. 2020. Exhaustive Entity Recognition for Coptic: Challenges and Solutions. In Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 19–28, Online. International Committee on Computational Linguistics.
Cite (Informal):
Exhaustive Entity Recognition for Coptic: Challenges and Solutions (Zeldes et al., LaTeCHCLfL 2020)
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PDF:
https://aclanthology.org/2020.latechclfl-1.3.pdf