Zobrazeno 1 - 5
of 5
pro vyhledávání: '"István Üveges"'
Autor:
Gergely Márk Csányi, Renátó Vági, Andrea Megyeri, Anna Fülöp, Dániel Nagy, János Pál Vadász, István Üveges
Publikováno v:
Information, Vol 14, Iss 10, p 520 (2023)
Few-shot learning is a deep learning subfield that is the focus of research nowadays. This paper addresses the research question of whether a triplet-trained Siamese network, initially designed for multi-class classification, can effectively handle m
Externí odkaz:
https://doaj.org/article/90b674e027254845a5aa0c77957992e2
Autor:
Gergely Márk Csányi, Renátó Vági, Dániel Nagy, István Üveges, János Pál Vadász, Andrea Megyeri, Tamás Orosz
Publikováno v:
Applied Sciences, Vol 12, Iss 3, p 1470 (2022)
One of the most time-consuming parts of an attorney’s job is finding similar legal cases. Categorization of legal documents by their subject matter can significantly increase the discoverability of digitalized court decisions. This is a multi-label
Externí odkaz:
https://doaj.org/article/64a4a46b0ec44a32a35b9ccb2b88eac3
Autor:
Tamás Orosz, Renátó Vági, Gergely Márk Csányi, Dániel Nagy, István Üveges, János Pál Vadász, Andrea Megyeri
Publikováno v:
Applied Sciences, Vol 12, Iss 1, p 297 (2021)
Many machine learning-based document processing applications have been published in recent years. Applying these methodologies can reduce the cost of labor-intensive tasks and induce changes in the company’s structure. The artificial intelligence-b
Externí odkaz:
https://doaj.org/article/618a2220a46e44628c413d722f218070
Autor:
Tomaž Erjavec, Maciej Ogrodniczuk, Petya Osenova, Petya Petya Osenova, Andrej Pancur, Nikola Ljubešic, Tommaso Agnoloni, StarkaDur Barkarson, María Calzada Pérez, Çagrı Çöltekin, Matthew Coole, Roberts Dargis, Macedo, Luciana D., Jesse de Does, Katrien Depuydt, Sascha Diwersy, Dorte Haltrup Hansen, Matyáš Kopp, Tomas Krilavicius, Giancarlo Luxardo, Maarten Marx, Vaidas Morkevicius, Costanza Navarretta, Paul Rayson, Orsolya Ring, Michał Rudolf, Kiril Simov, Steinþór Steingrímsson, István Üveges, Ruben van Heusden, Giulia Venturi
Publikováno v:
Erjavec, T, Ogrodniczuk, M, Osenova, P, Petya Osenova, P, Pancur, A, Ljubešic, N, Agnoloni, T, Barkarson, S, Calzada Pérez, M, Çöltekin, Ç, Coole, M, Dargis, R, de Macedo, L D, de Does, J, Depuydt, K, Diwersy, S, Hansen, D H, Kopp, M, Krilavicius, T, Luxardo, G, Marx, M, Morkevicius, V, Navarretta, C, Rayson, P, Ring, O, Rudolf, M, Simov, K, Steingrímsson, S, Üveges, I, van Heusden, R & Venturi, G 2021, ParlaMint: Comparable Corpora of European Parliamentary Data . in Proceedings of CLARIN Annual Conference 2021 . CLARIN ERIC, pp. 19-24 . < https://office.clarin.eu/v/CE-2021-1923-CLARIN2021_ConferenceProceedings.pdf >
University of Copenhagen
University of Copenhagen
This paper outlines the ParlaMint project from the perspective of its goals, tasks, participants, results and applications potential. The project produced language corpora from the sessions of the national parliaments of 17 countries, almost half a b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::4bc2f2116434532a5afc10e6928e202c
https://curis.ku.dk/ws/files/321465715/CE_2021_1923_CLARIN2021_ConferenceProceedings.pdf
https://curis.ku.dk/ws/files/321465715/CE_2021_1923_CLARIN2021_ConferenceProceedings.pdf
Autor:
Istvan Uveges, Orsolya Ring
Publikováno v:
IEEE Access, Vol 11, Pp 60267-60278 (2023)
The growing number of digitally accessible text corpora and the accelerating development of NLP tools and methods (particularly the emergence of powerful large-scale language models) have allowed their widespread use in various classification tasks,
Externí odkaz:
https://doaj.org/article/45fe2e7bf28f4dbf8a3037bbca82f439