Jacek Kudera


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LODinG: Linked Open Data in the Humanities
Jacek Kudera | Claudia Bamberg | Thomas Burch | Folke Gernert | Maria Hinzmann | Susanne Kabatnik | Claudine Moulin | Benjamin Raue | Achim Rettinger | Jörg Röpke | Ralf Schenkel | Kristin Shi-Kupfer | Doris Schirra | Christof Schöch | Joëlle Weis
Proceedings of the 9th Workshop on Linked Data in Linguistics @ LREC-COLING 2024

We are presenting LODinG – Linked Open Data in the Humanities (abbreviated from Linked Open Data in den Geisteswissenschaften), a recently launched research initiative exploring the intersection of Linked Open Data (LOD) and a range of areas of work within the Humanities. We focus on effective methods of collecting, modeling, linking, releasing and analyzing machine-readable information relevant to (digital) humanities research in the form of LOD. LODinG combines the sources and methods of digital humanities, general and computational linguistics, digital lexicography, German and Romance philology, translatology, cultural and literary studies, media studies, information science and law to explore and expand the potential of the LOD paradigm for such a diverse and multidisciplinary field. The project’s primary objectives are to improve the methods of extracting, modeling and analyzing multilingual data in the LOD paradigm; to demonstrate the application of the linguistic LOD to various methods and domains within and beyond the humanities; and to develop a modular, cross-domain data model for the humanities.


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Rediscovering the Slavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification
Badr M. Abdullah | Jacek Kudera | Tania Avgustinova | Bernd Möbius | Dietrich Klakow
Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects

Deep neural networks have been employed for various spoken language recognition tasks, including tasks that are multilingual by definition such as spoken language identification (LID). In this paper, we present a neural model for Slavic language identification in speech signals and analyze its emergent representations to investigate whether they reflect objective measures of language relatedness or non-linguists’ perception of language similarity. While our analysis shows that the language representation space indeed captures language relatedness to a great extent, we find perceptual confusability to be the best predictor of the language representation similarity.