The main aim of this Action is to promote synergies across Europe between linguists, computer scientists, terminologists, and other stakeholders in industry and society, in order to investigate and extend the area of linguistic data science. We understand linguistic data science as a subfield of the emerging “data science”, which focuses on the systematic analysis and study … Read more

Culturally Aware AI

The AI:CULT project addresses the gap between AI and our digital cultural heritage. Cultural heritage data is rarely objective data. The very reasons for certain heritage data to be preserved, its interpretation throughout time, and the way heritage data is accessed after digitalisation is all subject to strong biases. The inherent richness, subjectivity and polyvocal … Read more


Odeuropa will apply state-of-the-art AI techniques to cultural heritage text and image datasets spanning four centuries of European history, to identify and trace how ‘smell’ was expressed in different languages, with what places it was associated, what kinds of events and practices it characterised, and to what emotions it was linked. This multi-modal information will … Read more

Text as a Graph

Computers have become indispensable for the storage, dissemination, and representation of literary and historical sources. What is more, computers can be used not only to assist our research practice, but also as research instruments in and by themselves. This means we have to represent texts in a way that is computationally processable. This entails thinking … Read more

Data Scopes

Many large digital text collections and computational tools that are available online today, allow humanities scholars to address a wide array of research questions. This often involves many data transformations: gathering and selecting documents, extracting and modelling the relevant data in them, cleaning and normalising this data, and linking dispersed information both within the resource … Read more

Automated collation of literary and historical texts

Collation, an important step in scholarly editing, involves comparing two or more versions of a work. The aim of this project is on the one hand to reflect on the collation process, where we deal with questions such as “how do we define ‘textual variation’?” and “what are the methodological ramifications of automating this important … Read more

TRIFECTA Principal Investigator: Marieke van Erp