Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Pivovarova, Lidia"'
Autor:
Zosa, Elaine, Pivovarova, Lidia
This paper presents M3L-Contrast -- a novel multimodal multilingual (M3L) neural topic model for comparable data that maps texts from multiple languages and images into a shared topic space. Our model is trained jointly on texts and images and takes
Externí odkaz:
http://arxiv.org/abs/2211.08057
Morphological and syntactic changes in word usage (as captured, e.g., by grammatical profiles) have been shown to be good predictors of a word's meaning change. In this work, we explore whether large pre-trained contextualised language models, a comm
Externí odkaz:
http://arxiv.org/abs/2204.05717
Semantics, morphology and syntax are strongly interdependent. However, the majority of computational methods for semantic change detection use distributional word representations which encode mostly semantics. We investigate an alternative method, gr
Externí odkaz:
http://arxiv.org/abs/2109.10397
Autor:
Kutuzov, Andrey, Pivovarova, Lidia
We present a manually annotated lexical semantic change dataset for Russian: RuShiftEval. Its novelty is ensured by a single set of target words annotated for their diachronic semantic shifts across three time periods, while the previous work either
Externí odkaz:
http://arxiv.org/abs/2106.08294
This paper addresses methodological issues in diachronic data analysis for historical research. We apply two families of topic models (LDA and DTM) on a relatively large set of historical newspapers, with the aim of capturing and understanding discou
Externí odkaz:
http://arxiv.org/abs/2011.10428
Publikováno v:
WWW 20 Companion Proceedings of the Web Conference 2020 (April 2020) p. 343-349
The way the words are used evolves through time, mirroring cultural or technological evolution of society. Semantic change detection is the task of detecting and analysing word evolution in textual data, even in short periods of time. In this paper w
Externí odkaz:
http://arxiv.org/abs/2001.06629
Autor:
Liimatta, Aatu, Mäkelä, Eetu, Ginter, Filip, Rastas, Iiro, Tihonen, Iiro, Zhang, Jinbin, Pivovarova, Lidia, Tolonen, Mikko, K, Milja, Babbar, Rohit, Wang, Ruilin, Säily, Tanja, Ryan, Yann Ciarán
This paper combines text reuse detection (using software developed for protein string detection) with genre classification based on the Bert transformer model, to understand changing patterns of genre and intextuality in a large dataset of eighteenth
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::559a54ee7a91763875cef36888f30286
Publikováno v:
Kutuzov, Andrei Kuzmenko, Elizaveta Pivovarova, Lidia . Clustering of Russian Adjective-Noun Constructions using Word Embeddings. Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing. 2017, 3-13 Association for Computational Linguistics
Externí odkaz:
http://hdl.handle.net/10852/55264
https://www.duo.uio.no/bitstream/handle/10852/55264/1/clustering_constructions.pdf
https://www.duo.uio.no/bitstream/handle/10852/55264/1/clustering_constructions.pdf
Autor:
Kanner, Antti, Mäkelä, Eetu, Marjanen, Jani, Tolonen, Mikko, Oberbichler, Sarah, Duong, Quan, Pivovarova, Lidia, Ali, Dilawar, Verstockt, Steven, Ollion, Étienne, Shen, Rubing, Arnold, Matthias, Brown, David, Adam, Raven, Balasubramanian, Saranya, Charvat, Vera Maria, Füllsack, Manfred, Kleinert, Jörn, Misera, Hanna, Pantelic, Nenad, Sonnberger, Jakob, Vogelor, Georg, De Mulder, Alessandra, Kokko, Heikki, Resch, Claudia, Schlögl, Matthias, Rastinger, Nina C., Kampkaspar, Dario, Cuper, Mirjam, Alrahabi, Motasem, Lejeune, Gaël, Parfait, Caroline, Roe, Glenn, Birkholz, Julie M., Chambers, Sally, Hradiš, Michal, Smrz, Pavel, Vidal-Gorène, Chahan, Papastomkou, Sofia, Parr, Jessica, Quiroga, Riva, Sturgeon, Donald, Lee, Ben, Yarasavage, Nathan, Chagué, Alix, Chiffoleau, Floriane, Babitsky, Timlynn, Salmons, Jim, Hyman, James, Lomazow, Steven
This publication presents the abstracts of What’s Past is Prologue: The NewsEye International Conference organised by the consortium of the EU Horizon 2020 research and innovation programme project NewsEye: A Digital Investigator for Historical New
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8f4feff21343e9793ff801076759280f