AI-Supported Analysis of Cultural Exchange in Antiquity
The study of cultural and intellectual exchange in Antiquity presents a fundamental challenge for historians. Ancient societies, from the Greek city-states to the empires of Mesopotamia and Persia, were interconnected not only through trade and diplomacy but also through the transmission, reinterpretation, and adaptation of ideas, political concepts, and philosophical frameworks.
Research in this domain requires specialists in multiple ancient languages and scripts (e.g., Greek and cuneiform) to manually sift through vast, fragmentary textual corpora, often spending years identifying thematic parallels and conceptual shifts.
Recent advances in AI and semantic text analysis offer promising opportunities to support such research. While computational methods for ancient languages are still in their infancy due to limited training data and fragmented sources, they may help to systematically search for similarities across text corpora and could elevate historical research to a new level of efficiency and depth.
The research focus should include:
- Cross-Lingual Semantic Analysis: Investigating techniques for identifying semantically related concepts across different languages
- Large Language Models (LLMs): Evaluating the applicability of modern language models to low-resource and historical language corpora.
- Ancient Language Processing: Exploring approaches for working with Ancient Greek and cuneiform texts (e.g., Akkadian, Old Persian, and Elamite).
The thesis will be co-supervised by Ass. Prof. Julian Degen from the Institut für Alte Geschichte und Altorientalistik), an expert in Ancient History.
