Ancient Language Processing
Focus: Texts · NLP, computational linguistics, treebanks, ancient language processing
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In this track, we will explore how computational and AI-based methods can be applied to ancient textual sources. We will survey digital language resources for cuneiform, Akkadian, Latin, and Ancient Greek — from corpora such as ORACC, CDLI, and Perseus to annotated treebanks and NLP pipelines. We will learn how language models work: how words and sentences are represented as embeddings, how transformer architectures process text, and how supervised models are trained and evaluated on ancient language data. Building on these foundations, we will examine how to work effectively with large language models (LLMs) through prompt engineering techniques, including zero-shot, few-shot, chain-of-thought, and tree-of-thought prompting, as well as strategies for knowledge injection such as retrieval-augmented generation (RAG) and fine-tuning. Afternoon sessions are hands-on: participants will apply each method to real linguistic questions drawn from ancient Near Eastern and classical corpora, compare results across approaches, and present their findings at the end of the week. No prior programming experience is required; students are welcome to bring their own datasets and research questions.
Computer Vision for Material Culture
Focus: Material Culture · 2D Computer Vision & OCR, 3D Modeling, Photogrammetry
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In this track, we will use computational tools applied to 2D and 3D data to analyse physical objects and areas reflecting cultures across time and space. We will learn about foundational tools in computer vision, including state-of-the-art methods for classifying images, identifying and segmenting objects, and digitising physical text, applied to photographed and scanned artefacts and manuscripts. We will introduce methods for collecting, inferring, and visualising 3D models, showing how this can be used to analyse artefacts and spaces beyond flat images. Finally, we will introduce recent AI methods combining visual and textual input, such as multimodal LLMs, and show how these can be used to gain new insights on material culture. Lectures will be followed by hands-on sessions where participants practise skills such as applying computer vision algorithms using Python and Jupyter, and experimenting with 3D modelling tools such as GigaMesh.
Network Analysis for Ancient Cultures
Focus: Networks · Spatial and social network analysis, dynamic network visualization
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The Network Analysis track focuses on understanding how to study the dynamic, interactive aspects of ancient societies and model them as networks. We will look into various aspects of ancient culture, such as motion, trade, and the spread of goods, whether lexical or material. We will learn how to create and annotate network data. The tools we will experiment with will be open tools, such as programming packages and free software. No prior experience is needed; students are welcome to bring their own data, be it lexical, material, social, or spatial, from the ancient world. Otherwise, we will work with existing data relevant to your individual interests and field of expertise. Upon completing the track, you would be able to critically analyze networks across various disciplines in ancient and modern cultures. You will develop proficiency in editing and plotting network data formats and become acquainted with common methods practiced in network analysis.