The main aim of NexusLinguarum 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 of the structure and properties of data at a large scale, along with methods and techniques to extract new knowledge and insights from it. Linguistic data science is a specific case, which is concerned with providing a formal basis to the analysis, representation, integration and exploitation of language data (syntax, morphology, lexicon, etc.). In fact, the specificities of linguistic data are an aspect largely unexplored so far in a big data context.


The activities of the Action aim to foster collaboration and knowledge-sharing between the Action members, and include Short-Term Scientific Missions (STSMs), WG meetings, conferences and workshops, training schools, and other dissemination events.


The results of the Action include reports, publications, pointers to relevant systems and resources, as well as collaborations and bridges to related initiatives.


NexusLinguarum is composed of five working groups (WGs 1-5), interoperating and providing mutual feedback between themselves. The core group is responsible for the coordination and management of the whole network, and for the dissemination of its results.


The results of the Action include reports, publications, pointers to relevant systems and resources, as well as collaborations and bridges to related initiatives.

44 documents
  • Hugo Gonçalo Oliveira. (June, 2022). Exploring Transformers for Ranking Portuguese Semantic Relations. Zenodo.
  • Gonçalo Oliveira, Hugo. (March, 2022). Drilling Lexico-Semantic Knowledge in Portuguese from BERT. Zenodo.
  • Declerck, Thierry, McCrae, John P., Montiel, Elena, Chiarcos, Christian, Ionov, Max. (June, 2022). Proceedings of the 8thWorkshop on Linked Data in Linguistics (LDL-2022). Zenodo.
  • Declerck, Thierry. (June, 2022). Towards the Linking of a Sign Language Ontology with Lexical Data. Zenodo.
  • Bajčetić, Lenka, Yim, Seung-Bin, Declerck, Thierry. (June, 2022). Towards the Profiling of Linked Lexicographic Resources. Zenodo.
  • Declerck, Thierry. (June, 2022). Towards a new Ontology for Sign Languages. Zenodo.
  • Barbara Lewandowska-Tomaszczyk, Slavko Žitnik, Anna Bączkowska, Chaya Liebeskind, Giedre Valunaite Oleškevičiene, Marcin Trojszczak. (June, 2022). An offensive language taxonomy and a web corpus discourse analysis for automatic offensive language identification. Zenodo.
  • Anas Fahad Khan, Christian Chiarcos, Thierry Declerck, Daniela Gifu, Elena González-Blanco García, Jorge Gracia, Maxim Ionov, Penny Labropoulou, Francesco Mambrini, John P. McCrae, Émilie Pagé-Perron, Marco Passarotti, Salvador Ros Muñoz, Ciprian-Octavian Truica. (June, 2022). When Linguistics Meets Web Technologies. Recent advances in Modelling Linguistic Linked Open Data. Zenodo.
  • Goel, Shashwat, Gracia, Jorge, Forcada, Mikel Lorenzo. (June, 2022). Bilingual dictionary generation and enrichment via graph exploration. Zenodo.
  • Rackevičienė, Sigita, Utka, Andrius, Mockienė, Liudmila, Rokas, Aivaras. (November, 2021). Methodological Framework for the Development of an English-Lithuanian Cybersecurity Termbase. Zenodo.