  Entity recognition provides semantic access to ancient materials in the
Digital Humanities: itexposes people and places of interest in texts that
cannot be read exhaustively, facilitates linkingresources and can provide a
window into text contents, even for texts with no translations. Inthis paper we
present entity recognition for Coptic, the language of Hellenistic era Egypt.
Weevaluate 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 en-tity recognition and semi-automatic entity
linking to Wikipedia, relying on robust dependencyparsing, feature-based CRF
models, and hand-crafted knowledge base resources, enabling highaccuracy 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.
