A Pearson-Spearman approach for evaluating semantic similarity tasks

Nikolaichuk, A. I.

Публікація:
A Pearson-Spearman approach for evaluating semantic similarity tasks

dc.contributor.author	Nikolaichuk, A. I.
dc.date.accessioned	2025-05-02T16:26:12Z
dc.date.available	2025-05-02T16:26:12Z
dc.date.issued	2025
dc.description.abstract	This work evaluates the performance of Semantic Textual Similarity (STS) models across different language pairs and model characteristics. The comparison is conducted using both Pearson and Spearman correlation coefficients to mitigate the limitations of each. The results show that the newer models, GTE and MPNet, achieved the best performance, followed by MiniLM, which has a smaller embedding dimensionality. The findings indicate that model performance is influenced by a combination of factors, rather than a single one. Additionally, the study highlights the challenges of cross-lingual similarity assessment.
dc.identifier.citation	Nikolaichuk A. I. A Pearson-Spearman approach for evaluating semantic similarity tasks / A. I. Nikolaichuk ; науковий керівник к. т. н., ас. І. О. Кобилін // Радіоелектроніка та молодь у XXI столітті : матеріали 29-го Міжнар. молодіж. форуму, 16–19 квітня 2025 р. – Харків : ХНУРЕ, 2025. – Т. 7. – С. 110–112.
dc.identifier.uri	https://openarchive.nure.ua/handle/document/31020
dc.language.iso	en
dc.publisher	ХНУРЕ
dc.subject	semantic similarity
dc.subject	evaluation
dc.subject	pearson-spearman approach
dc.title	A Pearson-Spearman approach for evaluating semantic similarity tasks
dc.type	Thesis
dspace.entity.type	Publication