Ruse of reuse
Detecting text-similarity with AI in historical sources
The volume of text available in various digital corpora has grown immensely thanks to numerous ongoing digitization projects, and the continued success of HTR will only further expand these collections. Consequently, the need to take full advantage of these vast resources is more pressing than ever. The ability to identify text-reuse and measure text-similarity is thus more important than ever, offering the potential to see connections never viewed before. AI is the key to making this possible.This two-day workshop is a continuation of our 2023 event, Finding Connections: Using AI and DNA Sequencing to Find Similarities and Parallels in Medieval Texts, now with an extended focus on general historical sources.
March 5th will be devoted to research presentations across two sessions. In the afternoon, William Mattingly will deliver an associated KIMAFO Lecture: From Physical Object to Structured Data: Building AI Pipelines in Cultural Heritage.
March 6th will take a more informal, hands-on workshop approach. Two morning sessions will be dedicated to work-in-progress reports, plans for implementing AI in various projects, experimental approaches, and open discussions. The afternoon will conclude with a closed practical programming workshop focusing on the application of various AI methods, led by Martin Roček and Gleb Schmidt.
Organizers
- Jan Odstrčilík, Institute for Medieval Research, Austrian Academy of Sciences
- Martin Roček, Faculty of Arts, Charles University and Institute for Medieval Research, Austrian Academy of Sciences
- Gleb Schmidt, The Social Life of Early Medieval Normative Texts’ SOLEMNE (canones.org) (ERC Cons: 101087979), Radboud University
Organizing institutions
- Institute for Medieval Research, Austrian Academy of Sciences
- The Social Life of Early Medieval Normative Texts’ SOLEMNE (canones.org) (ERC Cons: 101087979), Radboud University
Cooperation
- Austrian Center for Digital Humanities, Austrian Academy of Sciences
- Machine Learning Topical Platform, Austrian Academy of Sciences