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

Dmitry Nikolaev, Jorke Grotenhuis, Haleli Harel, Orly Goldwasser

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

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 webbased 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.
Original languageEnglish
Title of host publicationFirst Machine Learning for Ancient Languages Workshop (ML4AL 2024)
Publication statusAccepted/In press - 24 Jun 2024

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