The Evolution of Distributional Semantics for Ancient Greek: From Vector Models to Transformer-Based Approaches – State of the Art and Perspectives on Statement Retrieval

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.