Word Embeddings for Detecting Lexical Semantic Change in Ukrainian

By December 19, 2024,
Page 231-243
Author Nataliia Cheilytko, Ruprecht von Waldenfels
Title Word Embeddings for Detecting Lexical Semantic Change in Ukrainian
Abstract The paper presents a count-based semantic vector space model for Ukrainian, which has been applied for the semantic change detection task. The approach assumes creation of multidimensional vector representations of occurrences for a particular lexeme or a group of related lexemes with further visual and quantitative analysis of the obtained semantic vector space. The multidimensional space has been reduced to 2D for visual data analysis with the Multidimensional Scaling technique. The paper described two case studies to show how the proposed R & D workflow helps revealing potential semantic change events and discuss benefits and limitations of the approach. One case study traces the disappearance of a regional sense, and another identifies the appearance of a new metaphoric sense that is widespread in the Ukrainian media discourse.
Session Talk
Keywords Ukrainian; corpus linguistics; GRAC; word embeddings; vector representation; semantic vector space model; semantic change; semasiological variation; multidimensional scaling; pointwise mutual information; semantic distance
BibTex 
@inproceedings{euralex_2024_paper_18,
address = {Cavtat},
title = {Word Embeddings for Detecting Lexical Semantic Change in Ukrainian},isbn = {978-953-7967-77-2},
shorttitle = {Euralex 2024},
url = {},
language = {eng},
booktitle = {Lexicography and Semantics. Proceedings of the XXI EURALEX International Congress},
publisher = {Institut za hrvatski jezik},
author = {Cheilytko, Nataliia and Waldenfels, Ruprecht von},
editor = {Despot, Kristina Š. and Ostroški Anić, Ana and Brač, Ivana},
year = {2024},
pages = {231-243}
}
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