Gábor Berend is an assistant professor at the Department of Algorithms and Artificial Intelligence, Institute of Informatics, University of Szeged. Within natural language processing his main research interest is focused on semantic aspects of computational linguistics. Besides natural language processing, he also has a keen interest in network science and coming up with algorithms that lie in the intersection of the two subfields.

Selected publications

  • Berend, Gábor. “Sparsity Makes Sense: Word Sense Disambiguation Using Sparse Contextualized Word Representations”. in the Proceedings of Conference on Empirical Methods in Natural Language Processing EMNLP-2020 (to appear)
  • Berend, Gábor. “Massively Multilingual Sparse Word Representations.” In International Conference on Learning Representations. 2020.
  • Balogh, Vanda, Gábor Berend, Dimitrios I. Diochnos, and György Turán. “Understanding the Semantic Content of Sparse Word Embeddings Using a Commonsense Knowledge Base.” In AAAI, pp. 7399-7406. 2020.
  • Berend, Gábor. “Sparse coding of neural word embeddings for multilingual sequence labeling.” Transactions of the Association for Computational Linguistics 5 (2017): 247-261.