Keynote Speakers
Filip Ginter is Professor of Computer Science at the University of Turku, Finland, co-founder of the TurkuNLP research group, and a faculty and management member at the ELLIS Institute Finland. His work focuses on core NLP methodology, data resources and their evaluation, high-performance computing, and large-scale multilingual and cross-lingual NLP, with particular interest in digital history research. He has contributed to many foundational NLP models and tools for Finnish, as well as to international efforts such as Universal Dependencies. Through the ELLIS Institute Finland and the national AI-DOC doctoral program in AI, he also helps advance Finland’s national AI ecosystem in general.
Keynote
AI for Digital History: The Algorithmic Challenges and Opportunities in Tracing Meaning at Scale
Abstract
Recent progress in AI and natural language processing (NLP) is reshaping what is possible in research. While public attention often focuses on chat-based AI, some of the most transformative opportunities lie in the high-performance computing–driven use of modern models on large and complex datasets. This is especially true in digital humanities and digital history, where computational methods can now address research questions that were previously beyond reach. Digital history is both scientifically important and computationally fascinating. It brings research questions that are rarely straightforward, alongside domain-specific challenges such as historical language variation, digitization noise, and massive multilingual text collections spanning centuries. Among the most compelling new directions is the emerging ability to trace meaning, i.e. how ideas spread, evolve, and influence one another across languages, regions, and historical periods. Modern AI and NLP models are beginning to make such analyses possible at scale, opening new ways of studying cultural and intellectual change. In this keynote, I will discuss key advances and the many remaining challenges in applying AI and NLP to digital history, with particular emphasis on large-scale cross-lingual tracing of meaning. I will argue that this domain is not only a valuable application area in its own right, but also a source of methods and perspectives that may prove inspiring beyond the digital humanities.