An expert system for evaluating language patterns using nonparametric statistics
DOI:
https://doi.org/10.26565/2304-6201-2025-68-11Keywords:
expert system, non-parametric statistical methods, language regularities, softwareAbstract
Abstract. The paper focuses on the research of language regularities by methods of non-parametric statistics. The emphasis is placed on how to resolve the common problems regarding the use of non-parametric statistical techniques, in particular the one of semantic marking of sentences in linguistics.
The aim of the scientific research is defined as the development of an expert system for evaluating language regularities using non-parametric statistical methods for any language.
Research methods. Methods of non-parametric statistics, IDEF4 notation, the programming language C #.
The research offers a specialized expert system that uses the C Programming Language to provide automatic questioning of the native speakers with further analysis of the lexical meaning of word combinations and phrases implemented as a Desktop Program for Windows. The program considers the possibility of taking into account of how to reveal the correspondence of phrases from dictionary files that have different expert assessments.
References
B. Strickland, “Language Reflects ‘Core’ Cognition: A New Theory About the Origin of Cross-Linguistic Regularities,” Cognitive Science, vol. 41, pp. 70–101, 2017. Available: https://onlinelibrary.wiley.com/doi/10.1111/cogs.12332
V. V. Zhukovska, “Cognitive-quantitative parametrization of positional properties of English detached impersonal/non-verbal constructions with explicit subject,” Zakarpatski Filolohichni Studii, vol. 17, no. 2, pp. 121–128, 2021. [in Ukrainian] Available: https://dspace.uzhnu.edu.ua/items/54938f63-cea9-43bd-b849-4c925e87811f
V. M. Zayats and M. M. Zayats, “Methods of comparing statistical characteristics in the formation of samples in linguistics,” Scientific Bulletin Journal of Lviv Polytechnic National University "Information Systems and Networks", no. 673, pp. 296–305, 2010. Available: https://ena.lpnu.ua/handle/ntb/6753
Ye. V. Kupriianov, N. S. Uholnikova, and O. M. Yurchenko, Structural linguistics in theory and practice. Kharkiv, Ukraine: NTU “KhPI”, 2024. [in Ukrainian]. Available: https://repository.kpi.kharkov.ua/handle/KhPI-Press/76037
W. Zhang, T. Yoshida, and X. Tang, “Text classification based on multi-word with support vector machine,” Knowledge-Based Systems, vol. 21, no. 8, pp. 879–886, 2008. Available: https://www.sciencedirect.com/science/article/abs/pii/S0950705108000968?via%3Dihub
A. Sitar, “Statistical analysis of phraseologized sentences: an indicator of mutual information association,” Ukrainian Linguistics. Kyiv: Taras Shevchenko National University. vol. 1, no. 46, pp. 114–125, 2016. [in Ukrainian] Available: https://doi.org/10.17721/um/46(2016).103-125
W. Zhang, T. Yoshida, and X. Tang, “Text classification based on multi-word with support vector machine,” Knowledge-Based Systems, vol. 21, no. 8, pp. 879–886, 2008. Available: https://www.researchgate.net/publication/222219356_Text_classification_based_on_multi-word_with_support_vector_machine#fullTextFileContent
V. Lytvyn, V. Vysotska, and M. H. Hrendus, “Method of data expression from the Ukrainian content based on the ontological approach,” Radio Electronics, Computer Science, Control, no. 3, vol. 2362, pp. 53–70, 2018. Available: https://ric.zp.edu.ua/article/view/149991
J. Skeet, C# in Depth, 4th ed. Shelter Island, NY: Manning, 2019. Available: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://dl.ebooksworld.ir/motoman/Manning.Csharp.in.Depth.4th.Edition.www.EBooksWorld.ir.pdf
