Slavic (and Serbian) Literary and Linguistic Studies in the Light of AI Revolution
In the evolving landscape of artificial intelligence and digital technologies, the intersection of language, literature, and computational tools offers new perspectives for Slavic studies. This lecture explores how AI-driven innovations are transforming traditional approaches to linguistic and literary analysis, focusing on the contributions of the TESLA (Text Embeddings Serbian Language Applications) project and the Jerteh Society's efforts in creating advanced language resources for the Serbian language. Developed resources are useful not only for Serbian but also for closely related languages. Several resources and their applications will be presented, like Serbian monolingual, comparable, and parallel corpora, a digital library for parallel text collections, the lexical database, wordnets, and a set of language models and their applications. The aforementioned resources pave the way for successful inclusion in the AI revolution through the development of language models and their integration into practical solutions. The talk examines the role of AI as a creative catalyst and analytical tool, where digital tools and theoretical models provide fresh insights into textual interpretation. The presentation highlights examples of generative AI applications in electronic literature and digital arts, showcasing their impact on the evolution of expressive languages within Slavic studies. Additionally, the role of AI in language teaching, translation, and human-machine interaction is discussed, with practical examples illustrating how the TESLA project's resources enhance these fields. By bridging tradition and innovation, this lecture aims to inspire dialogue on the future of the humanities in the AI era, emphasizing the transformative power of technology in preserving and reimagining Slavic linguistic and cultural heritage.
Institutions
- University of Belgrade (RS)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-03-27
- DOI
- https://doi.org/10.5281/zenodo.15093653
- Primary Topic
- Digital Humanities and Scholarship
- Type
- article
- Field-Weighted Citation Impact
- 0.00