古埃及语中的分类符识别：一项低资源序列标注任务
Classifier identification in Ancient Egyptian as a low-resource sequence-labelling task
AI总结：
本文将古埃及语分类符识别形式化为低资源序列标注任务，提出多种神经序列标注模型，在有限训练数据下取得有前景的性能，并与频率基线进行对比。
摘要：
复杂的古埃及语（AE）书写系统的特点是广泛使用字素分类符（定符）：即无声（不发音）的圣书体符号，用于澄清宿主词的含义或指示其发音。近年来，随着iClassifier项目的启动和快速发展，对分类符的研究不断加强；iClassifier是一个基于网络的平台，用于对古代和现代语言中的分类符进行标注和分析。得益于项目参与者贡献的数据，现在可以将古埃及语文本中的分类符识别表述为一项自然语言处理（NLP）任务。在本文中，我们通过实现一系列序列标注神经模型，朝着解决这一任务迈出了第一步；尽管训练数据量有限，这些模型仍取得了有前景的性能。我们讨论了处理古埃及语文本时出现的分词和可操作性问题，并将我们的方法与基于频率的基线进行了对比。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
