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The Ancient Egyptian (AE) writing system was characterised by widespread use of graphemic classifiers: 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, which provided a web-based platform for annotation and analysis of classifiers in hieroglyphic, cuneiform, and ancient Chinese texts. 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.

ACL 2024

Classifier identification in Ancient Egyptian as a low-resource sequence-labelling task

ancient scripts

ancient egyptian

classifiers

sequence labelling

workshop paper

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

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Classifier identification in Ancient Egyptian as a low-resource sequence-labelling task

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A deep learning pipeline for the palaeographical dating of ancient Greek papyrus fragments

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