Tabea Anreiter

Resources

• Why Does Language Matter in DH? Multilingual DH as a Phenomenon in Digital Research and Infrastructures

  EN

  This lecture explores how Large Language Models represent meaning, form generalisations and reflect social perspectives. It examines the extent to which language models can perform semantic interpretation and abstraction, how stereotypes may be represented in their outputs, and how the language used to describe AI influences our understanding of its capabilities.

• Immersive AR/VR for Collaborative Heritage Exploration and Decision-Making

  EN

  This learning resource explores how Augmented Reality (AR) and Virtual Reality (VR) can support collaboration in cultural heritage preservation and management. Learners examine how immersive technologies enable contextualised visualisation, remote interaction, and shared exploration of heritage assets. Through practical examples, the resource highlights opportunities and challenges of integrating AR and VR into heritage practices, while encouraging critical reflection on their potential to enhance interpretation, decision-making, and sustainable heritage management.

• From Punch Cards to Prompt Engineering: The MHDBDB and the Future of Semantic Annotation with LLMs

  EN

  This lecture explores the technological evolution of the Middle High German Conceptual Database (MHDBDB) and its transition towards a TEI-first architecture. It examines how structured, machine-readable data and Large Language Models can support semantic annotation and Expert-in-the-Loop research workflows in the Digital Humanities.

• Exploring the Boundaries: The Present and Future of AI-powered Handwritten Text Recognition

  EN

  This lecture explores the current capabilities and limitations of AI-powered Handwritten Text Recognition (HTR). It examines multilingual HTR across different scripts and languages, introduces more advanced applications such as abbreviation expansion and transliteration, and considers the impact of large-scale text recognition on humanities research.

• Enabling Intelligent Services with Knowledge Graphs and AI

  EN

  This resource explores how Knowledge Graphs and Artificial Intelligence can be combined to make complex cultural heritage information more accessible, searchable, and trustworthy. It introduces graph structures, the limitations of Large Language Models, and the ARTEMIS Artemisia Oracle pipeline for transforming unstructured documentation into ontology-aligned knowledge. It also examines vector embeddings, semantic similarity search, natural-language exploration, and the role of structured knowledge and human validation in developing intelligent services for Heritage Digital Twins.

• Digital Linguistics

  EN

  This lecture introduces Digital Linguistics as an interdisciplinary field concerned with the analysis, preservation and dissemination of digital language data. It explores the development of the field, its relationship with Digital Humanities and Computational Linguistics, and current and future challenges surrounding digital language resources.

• Digital Humanities and the (Unsurprising?) Tenacity of the Print Mind

  EN

  This lecture explores how ideas inherited from print culture continue to shape the way digital texts are created, organised and studied. It examines the implications of the “print mind” for digital editions, linguistic corpora, metadata, annotation and research practices in the Digital Humanities.

• Culturomics and Construction Grammar

  EN

  This lecture explores culturomics as an approach to the quantitative analysis of large textual datasets. It examines limitations of token-based methods and shows how concepts from construction grammar can provide a richer understanding of linguistic patterns and their cultural significance.

• Contextualizing Museum Data: Digitization, Infrastructure and Digital Literacy

  EN

  This lecture explores the processes and infrastructures that shape digital museum data. It examines digitization, data management and digital literacy, highlighting how the contexts in which cultural heritage data is created and stored influence its interpretation and use.

• Complexity, Feasibility, and Validation in Digital Twin and Spatial Simulation Projects

  EN

  Complexity is inherent in digital twin and spatial simulation projects. The ARTEMIS case studies show that it emerges through actors, data flows, technologies, and uncertainty, often becoming visible only during implementation. Successful projects make this complexity explicit, define validation criteria early, and stay aligned with the research question and user needs. This ensures that development remains not only technically functional, but also scientifically meaningful and practically valuable.

• Beyond Correlation: Causal AI Decisions Based on World Models

  EN

  This lecture explores how causal AI can move beyond correlation-based approaches by combining explicit world models, knowledge graphs and semantic technologies. It examines how causal reasoning can support more transparent, traceable and reliable AI decision-making and introduces deepassist as an example of this approach.