AUTHORS: Federico IEZZI and Elia SCAPINI

Work Package: WP4 – DaMSym

URL: https://www.ai-lc.it/wp-content/uploads/2026/09/IJCoL_12_1_04.pdf

Keywords:

Abstract

Transformer-based Pre-trained Language Models (PLMs) for Ancient Greek are rapidly advancing in both number and performance. Among the opportunities enabled by these models is
semantic statement retrieval, but significant efforts to refine the technique and make it accessible
to the scholarly community are still in their early stages. This survey reviews advancements in
computational approaches to Ancient Greek semantics, describing the transition from traditional
vector-based models to state-of-the-art transformer-based architectures, with a particular focus
on their application to statement retrieval. It examines advantages and limitations of applying
transformer-based PLMs to tasks such as the study of semantic change, lexical analysis, and
statement retrieval. Although these models provide innovative methodologies for addressing
semantics in the analysis of Ancient Greek sources, challenges such as limited training data,
the absence of shared evaluation standards, and the “black box” nature of deep learning remain
significant obstacles. The paper highlights the potential of these technologies to enhance digital
scholarship on ancient texts and calls for further refinement and benchmarking to overcome
existing limitations