Neural Models for Lemmatization and POS-Tagging of Earlier and Late Egyptian (Supporting Hieroglyphic Input) and Demotic

AbstractWe present updated models for BabyLemma-tizer for lemmatizing and POS-tagging De-motic, Late Egyptian and Earlier Egyptian with a support for using hieroglyphs as an input. In this paper, we also use data that has not been cleaned from breakages. We achieve consistent UPOS tagging accuracy of 94% or higher and an XPOS tagging accuracy of 93% and higher for all languages. For lemmatization, which is challenging in all of our test languages due to extensive ambiguity, we demonstrate accu-racies from 77% up to 92% depending on the language and the input script.