# Tracking Semantic Change in Slovene: A Novel Dataset and Optimal Transport-Based Distance

Source: [Tracking Semantic Change in Slovene: A Novel Dataset and Optimal Transport-Based Distance](<https://www.sota2.com/research/paper/68fa78d21e209131cea7a972>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:47:07.371Z

Authors: Marko Pranji\\'c, Kaja Dobrovoljc, Senja Pollak, Matej Martinc

Published: 2024-02-26T14:27:06.000Z

## Abstract

In this paper, we focus on the detection of semantic changes in Slovene, a less resourced Slavic language with two million speakers. Detecting and tracking semantic changes provides insight into the evolution of language caused by changes in society and culture. We present the first Slovene dataset for evaluating semantic change detection systems, which contains aggregated semantic change scores for 104 target words obtained from more than 3,000 manually annotated sentence pairs. We analyze an important class of measures of semantic change metrics based on the Average pairwise distance and identify several limitations. To address these limitations, we propose a novel metric based on regularized optimal transport, which offers a more robust framework for quantifying semantic change. We provide a comprehensive evaluation of various existing semantic change detection methods and associated semantic change measures on our dataset. Through empirical testing, we demonstrate that our proposed approach, leveraging regularized optimal transport, achieves either matching or improved performance compared to baseline approaches.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Measuring magnitude of word-level semantic change on DWUG English](<https://www.sota2.com/research/sota/measuring-magnitude-of-word-level-semantic-change-on-dwug-english.md>): Measuring magnitude of word-level semantic change; DWUG English; Spearman Correlation: 0.71.

- [Word-level semantic change magnitude measurement on DWUG Spanish 2022](<https://www.sota2.com/research/sota/word-level-semantic-change-magnitude-measurement-on-dwug-spanish-2022.md>): Word-level semantic change magnitude measurement; DWUG Spanish 2022; Spearman Correlation: 0.61.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2402.16596>)

[PDF](<https://arxiv.org/pdf/2402.16596.pdf>)