Classifier identification in Ancient Egyptian as a low-resource sequence-labelling task
This work addresses a specific challenge in computational linguistics for Egyptology, representing an incremental step in applying NLP to ancient languages.
The paper tackles the problem of identifying graphemic classifiers in Ancient Egyptian texts as a low-resource NLP task, achieving promising performance with neural sequence-labeling models despite limited training data.
The complex Ancient Egyptian (AE) writing system was characterised by widespread use of graphemic classifiers (determinatives): silent (unpronounced) hieroglyphic signs clarifying the meaning or indicating the pronunciation of the host word. The study of classifiers has intensified in recent years with the launch and quick growth of the iClassifier project, a web-based platform for annotation and analysis of classifiers in ancient and modern languages. Thanks to the data contributed by the project participants, it is now possible to formulate the identification of classifiers in AE texts as an NLP task. In this paper, we make first steps towards solving this task by implementing a series of sequence-labelling neural models, which achieve promising performance despite the modest amount of training data. We discuss tokenisation and operationalisation issues arising from tackling AE texts and contrast our approach with frequency-based baselines.