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pro vyhledávání: '"A. Arefyev"'
This paper describes our solution of the first subtask from the AXOLOTL-24 shared task on Semantic Change Modeling. The goal of this subtask is to distribute a given set of usages of a polysemous word from a newer time period between senses of this w
Externí odkaz:
http://arxiv.org/abs/2408.05184
Autor:
Kokosinskii, Denis, Arefyev, Nikolay
Word Sense Induction (WSI) is the task of discovering senses of an ambiguous word by grouping usages of this word into clusters corresponding to these senses. Many approaches were proposed to solve WSI in English and a few other languages, but these
Externí odkaz:
http://arxiv.org/abs/2405.11086
Lexical Semantic Change Detection (LSCD) is a complex, lemma-level task, which is usually operationalized based on two subsequently applied usage-level tasks: First, Word-in-Context (WiC) labels are derived for pairs of usages. Then, these labels are
Externí odkaz:
http://arxiv.org/abs/2404.00176
We present a dataset of word usage graphs (WUGs), where the existing WUGs for multiple languages are enriched with cluster labels functioning as sense definitions. They are generated from scratch by fine-tuned encoder-decoder language models. The con
Externí odkaz:
http://arxiv.org/abs/2403.18024
Autor:
de Gibert, Ona, Nail, Graeme, Arefyev, Nikolay, Bañón, Marta, van der Linde, Jelmer, Ji, Shaoxiong, Zaragoza-Bernabeu, Jaume, Aulamo, Mikko, Ramírez-Sánchez, Gema, Kutuzov, Andrey, Pyysalo, Sampo, Oepen, Stephan, Tiedemann, Jörg
We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive
Externí odkaz:
http://arxiv.org/abs/2403.14009
Autor:
Kudisov, Artem, Arefyev, Nikolay
Publikováno v:
Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change, pages 165-172, Dublin, Ireland. 2022
We propose a solution for the LSCDiscovery shared task on Lexical Semantic Change Detection in Spanish. Our approach is based on generating lexical substitutes that describe old and new senses of a given word. This approach achieves the second best r
Externí odkaz:
http://arxiv.org/abs/2206.11865
Publikováno v:
Proceedings of the 28th International Conference on Computational Linguistics, pages 1242-1255, Barcelona, Spain (Online). International Committee on Computational Linguistics. 2020
Lexical substitution, i.e. generation of plausible words that can replace a particular target word in a given context, is an extremely powerful technology that can be used as a backbone of various NLP applications, including word sense induction and
Externí odkaz:
http://arxiv.org/abs/2206.11815
Autor:
Bingyu, Zhang, Arefyev, Nikolay
Publikováno v:
Proceedings of the Third Workshop on Insights from Negative Results in NLP, pages 129-133, Dublin, Ireland. Association for Computational Linguistics. 2022
The current state-of-the-art test accuracy (97.42\%) on the IMDB movie reviews dataset was reported by \citet{thongtan-phienthrakul-2019-sentiment} and achieved by the logistic regression classifier trained on the Document Vectors using Cosine Simila
Externí odkaz:
http://arxiv.org/abs/2205.13357
Publikováno v:
Bian, Jie Welzl, Michael Kutuzov, Andrey Arefyev, Nikolay . Tell Me Why: Language Models Help Explain the Rationale Behind Internet Protocol Design. 2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN). 2024 IEEE conference proceedings
Externí odkaz:
http://hdl.handle.net/10852/113712
Publikováno v:
van der Aalst W. et al. (eds) Analysis of Images, Social Networks and Texts. AIST 2019. Lecture Notes in Computer Science, vol 11832. Springer, Cham
Word sense induction (WSI) is the problem of grouping occurrences of an ambiguous word according to the expressed sense of this word. Recently a new approach to this task was proposed, which generates possible substitutes for the ambiguous word in a
Externí odkaz:
http://arxiv.org/abs/2006.13200