Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Kamil Kanclerz"'
Autor:
Przemysław Kazienko, Julita Bielaniewicz, Marcin Gruza, Kamil Kanclerz, Konrad Karanowski, Piotr Miłkowski, Jan Kocoń
Publikováno v:
Information Fusion. 94:43-65
Autor:
Julita Bielaniewicz, Kamil Kanclerz, Piotr Milkowski, Marcin Gruza, Konrad Karanowski, Przemyslaw Kazienko, Jan Kocon
Publikováno v:
2022 IEEE International Conference on Data Mining Workshops (ICDMW).
Autor:
Maciej Piasecki, Kamil Kanclerz
Publikováno v:
Computational Collective Intelligence ISBN: 9783031160134
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c427da1b55f7c75ba296166e463e5c95
https://doi.org/10.1007/978-3-031-16014-1_16
https://doi.org/10.1007/978-3-031-16014-1_16
Autor:
Maciej Piasecki, Kamil Kanclerz
Publikováno v:
Computational Science – ICCS 2022 ISBN: 9783031087509
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a10749aabcb746e5996a207b1ec82a2c
https://doi.org/10.1007/978-3-031-08751-6_18
https://doi.org/10.1007/978-3-031-08751-6_18
Autor:
Przemysław Kazienko, Julita Bielaniewicz, Marcin Gruza, Kamil Kanclerz, Konrad Karanowski, Piotr Miłkowski, Jan Kocoń
Publikováno v:
SSRN Electronic Journal.
Autor:
Kamil Kanclerz, Maciej Piasecki
Publikováno v:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop.
Publikováno v:
KES
In this article, we present a novel technique for the use of language-agnostic sentence representations to adapt the model trained on texts in Polish (as a low-resource language) to recognize polarity in texts in other (high-resource) languages. The
Autor:
Jan Kocon, Marcin Gruza, Julita Bielaniewicz, Damian Grimling, Kamil Kanclerz, Piotr Milkowski, Przemyslaw Kazienko
Publikováno v:
2021 IEEE International Conference on Data Mining (ICDM).
Autor:
Tomasz Kajdanowicz, Przemysław Kazienko, Marcin Gruza, Kamil Kanclerz, Daria Puchalska, Jan Kocoń, Alicja Figas
Publikováno v:
ACL/IJCNLP (1)
Scopus-Elsevier
Scopus-Elsevier
There is content such as hate speech, offensive, toxic or aggressive documents, which are perceived differently by their consumers. They are commonly identified using classifiers solely based on textual content that generalize pre-agreed meanings of
Publikováno v:
Computational Science – ICCS 2021 ISBN: 9783030779634
ICCS (2)
ICCS (2)
This article presents MultiEmo, a new benchmark data set for the multilingual sentiment analysis task including 11 languages. The collection contains consumer reviews from four domains: medicine, hotels, products and university. The original reviews
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2c911f093b9c0287088e89eec81472cc
https://doi.org/10.1007/978-3-030-77964-1_24
https://doi.org/10.1007/978-3-030-77964-1_24