Exploring Word Sense Distribution in Ukrainian with a Semantic Vector Space Model

Nataliia Cheilytko, Ruprecht von Waldenfels


Abstract
The paper discusses a Semantic Vector Space Model targeted at revealing how Ukrainian word senses vary and relate to each other. One of the benefits of the proposed semantic model is that it considers second-order context of the words and, thus, has more potential to compare and distinguish word senses observed in a unique concordance line. Combined with visualization techniques, this model makes it possible for a lexicographer to explore the Ukrainian word senses distribution on a large-scale. The paper describes the first results of the research performed and the following steps of the initiative.
Anthology ID:
2023.unlp-1.9
Volume:
Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editor:
Mariana Romanyshyn
Venue:
UNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
73–78
Language:
URL:
https://aclanthology.org/2023.unlp-1.9
DOI:
10.18653/v1/2023.unlp-1.9
Bibkey:
Cite (ACL):
Nataliia Cheilytko and Ruprecht von Waldenfels. 2023. Exploring Word Sense Distribution in Ukrainian with a Semantic Vector Space Model. In Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP), pages 73–78, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
Exploring Word Sense Distribution in Ukrainian with a Semantic Vector Space Model (Cheilytko & von Waldenfels, UNLP 2023)
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PDF:
https://aclanthology.org/2023.unlp-1.9.pdf
Video:
 https://aclanthology.org/2023.unlp-1.9.mp4