Classifier identification in A ncient E gyptian as a low-resource sequence-labelling task (2024)

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 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.

## introduction the ancient egyptian language and writing system, which belong to the earliest stratum of intangible cultural heritage available to researchers, possess a range of interesting features. one of them is widespread use of classificatory signs, called determinatives in earlier literature. these classifiers (hereafter clfs in ambiguous contexts, in order to avoid confusion with classifier models) are hieroglyphic signs attached, singly or in combinations, to words of different parts of speech and used mostly to highlight some aspect of the host word's meaning or pronunciation @xcite . egyptian graphemic classifiers are usually understood to be a purely written phenomenon, i.e., unlike classifiers in contemporary spoken languages @xcite , they were not pronounced. classifiers of this type have been most intensively studied in ancient egyptian, but they have been also described in sumerian @xcite and luwian @xcite , and it is argued that the ancient chinese writing system was built on similar principles @xcite . the computational research on the ancient egyptian language is in its infancy. a comprehensive overview of studies of ancient languages utilising machine-learning methods, prepared by @xcite , mentions only a couple of works on egyptian, and all of them deal with technical tasks, such as optical character recognition and spectrography-based dating. neither do we know of any computational works tackling classifiers/determinatives in other ancient scripts. at the same time, the field of classifier studies has been progressing rapidly in recent years. to a large extent this is due to the launch of iclassifier @xcite , a dedicated platform for analysis of classifiers in ancient and spoken languages, which ensures comparability between annotated corpora. by providing such a platform, the project aims to facilitate both the study of individual classification traditions and, by means of semantic annotations with concepticon labels @xcite , cross-cultural analyses of classification systems. the particular structure of any given corpus is dependent on its creator, and the project includes resources of two basic types: corpora of the first type are more informative, but in practice they presuppose the existence of alreadydigitised texts that can be imported in iclassifier wholesale and then annotated. in some cases, the target texts have not yet been digitised, and only words or phrases of particular interest are manually entered. work on projects of both types could be facilitated by the existence of a trained classifier model, which would highlight potential clf tokens in inputs. if such a classifier attains a high degree of accuracy, it will then be possible to conduct fast analyses of large digitised textual corpora, which have been published for, e.g., ancient egyptian (richter and werning, 2024), sumerian, foot_0 luwian, foot_1 and ancient chinese @xcite . from the research perspective, an accurate discriminative classifier model will serve as a first step towards building a more interpretable generative model for word classification in ancient complex scripts and spoken languages. in this study, we take first steps towards developing such a classifier on the basis of the coffin texts corpus, as of today the largest annotated full-text corpus in the iclassifier system. ## methods in this section, we describe our approaches to input tokenisation and output formatting ( § 3.1), the baselines ( § 3.2), and the experimental setup ( § 3.3). ## results the performance of the trained models on the development and test subsets of the coffin texts cor12 the code and the dataset used for the analyses are available at https://git.sr.ht/~macleginn/ ml4al-iclassifier-paper-code/tree ## conclusion this study is a first step towards creating a trained system for identification and analysis of classifiers and other sign functions in ancient complex scripts. it demonstrates that it is possible to achieve respectable error rates on this task on in-domain data, with ≈ 0.1 mistakenly identified classifiers per data point. given a high number of data points with several classifiers (cf. future work, in addition to improving model accuracy, could be directed toward providing a more fine-grained classification of sign functions by leveraging the distinction between semantic and grammatical classifiers and phono-repeaters.