Epigraphy is witnessing a growing integration of artificial intelligence, notably through its subfield of machine learning (ML), especially in tasks like extracting insights from ancient inscriptions. However, scarce labeled data for training ML algorithms severely limits current techniques, especially for ancient scripts like Old Aramaic. Our research pioneers an innovative methodology for generating synthetic training data tailored to Old Aramaic letters. Our pipeline synthesizes photo-realistic Aramaic letter datasets, incorporating textural features, lighting, damage, and augmentations to mimic real-world inscription diversity. Despite minimal real examples, we engineer a dataset of 250 000 training and 25 000 validation images covering the 22 letter classes in the Aramaic alphabet. This comprehensive corpus provides a robust volume of data for training a residual neural network (ResNet) to classify highly degraded Aramaic letters. The ResNet model demonstrates 95% accuracy in classifying real images from the 8th century BCE Hadad statue inscription. Additional experiments validate performance on varying materials and styles, proving effective generalization. Our results validate the model’s capabilities in handling diverse real-world scenarios, proving the viability of our synthetic data approach and avoiding the dependence on scarce training data that has constrained epigraphic analysis. Our innovative framework elevates interpretation accuracy on damaged inscriptions, thus enhancing knowledge extraction from these historical resources.
The paper describes the main challenges faced, and the solutions adopted in the frame of the project DASI - Digital Archive for the study of pre-Islamic Arabian inscriptions. In particular, the methodological and technological issues emerged in the conversion from a domain-specific text-based project of digital edition of an epigraphic corpus, to an objective-driven archive for the study and dissemination of inscriptions in different languages and scripts are discussed. With a view to keeping pace with, and possibly fostering reasoning on best practices in the community of digital epigraphers beyond each specific cultural/linguistic domain, special attention is devoted to: the modelling of data and encoding (XML annotation vs database approach; the conceptual model for the valorization of the material aspect of the epigraph; the textual encoding for critical editions); interoperability (pros and cons of compliance to standards; harmonization of metadata; openness; semantic interoperability); lexicography (tools for under-resourced languages; translations).
The Tesserae Project offers a free online intertextual search tool for ancient Greek, Latin, and English. Tesserae has in the past allowed for a pairwise searching of literary texts in these languages for exact word or lemma similarities. This paper describes two new types of search now offered by Tesserae, by meaning (semantic search) and by sound.
This article is a report about the progress and current status of the World Historical Gazetteer (whgazetteer.org) (WHG) in the context of its value for helping to organize and record digital and paleographic information. It summarizes the development and functionality of the WHG as a software platform for connecting specialist collections of historical place names. It also reviews the idea of places as entities (rather than simple objects with single labels). It also explains the utility of gazetteers in digital library infrastructure and describes potential future developments.
Recent years have seen an exponential growth (+98% in 2022 w.r.t. the previous year) of the number of research articles in the few-shot learning field, which aims at training machine learning models with extremely limited available data. The research interest toward few-shot learning systems for Named Entity Recognition (NER) is thus at the same time increasing. NER consists in identifying mentions of pre-defined entities from unstructured text, and serves as a fundamental step in many downstream tasks, such as the construction of Knowledge Graphs, or Question Answering. The need for a NER system able to be trained with few-annotated examples comes in all its urgency in domains where the annotation process requires time, knowledge and expertise (e.g., healthcare, finance, legal), and in low-resource languages. In this survey, starting from a clear definition and description of the few-shot NER (FS-NER) problem, we take stock of the current state-of-the-art and propose a taxonomy which divides algorithms in two macro-categories according to the underlying mechanisms: model-centric and data-centric. For each category, we line-up works as a story to show how the field is moving toward new research directions. Eventually, techniques, limitations, and key aspects are deeply analyzed to facilitate future studies.
This contribution presents a novel approach to the development and evaluation of transformer-based models for Named Entity Recognition and Classification in Ancient Greek texts. We trained two models with annotated datasets by consolidating potentially ambiguous entity types under a harmonized set of classes. Then, we tested their performance with out-of-domain texts, reproducing a real-world use case. Both models performed very well under these conditions, with the multilingual model being slightly superior on the monolingual one. In the conclusion, we emphasize current limitations due to the scarcity of high-quality annotated corpora and to the lack of cohesive annotation strategies for ancient languages.
This paper provides an overview of diverse applications of parallel corpora in ancient languages, particularly Ancient Greek. In the first part, we provide the fundamental principles of parallel corpora and a short overview of their applications in the study of ancient texts. In the second part, we illustrate how to leverage on parallel corpora to perform various NLP tasks, including automatic translation alignment, dynamic lexica induction, and Named Entity Recognition. In the conclusions, we emphasize current limitations and future work.