Linguistic Foundations Of Electronic Thesaurus Construction: Structure, Synset-Formation Methodology, And Corpus-Based Evidence From The Uzbek Language
Authors
Files
Key Information
Journal
Published in Vol. 6 No. 1 (2026) of Asian Journal of Academic Research.
Asian Journal of Academic Research (AJAR) (ISSN: 2313-6596) is an international, peer-reviewed, open-access scholarly journal dedicated to publishing high-quality, original research across a broad range of academic disciplines. The journal provides a global platform for researchers, academics, professionals, and policymakers to disseminate innovative findings, exchange knowledge, and promote interdisciplinary collaboration.
About the JournalAbstract
This article presents a comprehensive linguistic and technological analysis of the principles underlying the construction of electronic thesauri. The purpose of the study is threefold: (1) to characterise the structural and functional properties that distinguish an electronic thesaurus from a traditional dictionary; (2) to develop and empirically validate a step-by-step methodology for selecting thesaurus units and forming synsets, integrating corpus statistics, distributional semantics and transformer-based (BERT-type) models with expert verification; and (3) to describe how polysemy, synonymy, homonymy and ontological categorisation are represented within the resulting thesaurus model for four major word classes — object nouns, attributive adjectives, verbs of action/state, and adverbs. Descriptive, componential, distributive, ideographic, ontological and quantitative methods of analysis were applied to material drawn from the Uzbek Educational Corpus and the ARANEUM_UZBEKICUM corpus. As a result, 77 synsets were constructed across five lexical categories, three structural models of thesaurus organisation (hierarchical tree, network graph, and matrix) were formalised, and a six-stage synset-formation methodology was proposed and tested. The findings provide a replicable linguistic foundation for building electronic thesauri for Uzbek and typologically related agglutinative languages, with direct applications in information retrieval, machine translation and natural-language-processing systems.
References
1. Karaulov Yu.N. Общая и русская идеография. – Moscow: Nauka, 1976. – 356 p.
2. Miller G.A. WordNet: A Lexical Database for English // Communications of the ACM. – 1995. – Vol. 38, No. 11. – P. 39–41.
3. Fellbaum C. (ed.) WordNet: An Electronic Lexical Database. – Cambridge, MA: MIT Press, 1998. – 423 p.
4. Devlin J., Chang M.-W., Lee K., Toutanova K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding // Proceedings of NAACL-HLT. – 2019. – P. 4171–4186.
5. Vaswani A. et al. Attention Is All You Need // Advances in Neural Information Processing Systems. – 2017. – Vol. 30.
6. Mikolov T., Chen K., Corrado G., Dean J. Efficient Estimation of Word Representations in Vector Space // arXiv:1301.3781. – 2013.
7. Berners-Lee T., Hendler J., Lassila O. The Semantic Web // Scientific American. – 2001. – Vol. 284, No. 5. – P. 34–43.
8. Gruber T.R. A Translation Approach to Portable Ontology Specifications // Knowledge Acquisition. – 1993. – Vol. 5, No. 2. – P. 199–220.
9. Апресян Ю.Д. Лексическая семантика: синонимические средства языка. – Moscow: Nauka, 1974. – 367 p.
10. Kasares J. Diccionario ideológico de la lengua española. – Barcelona: Gustavo Gili, 1942.
11. Najmiddinov M.G'. Tezauruslar yaratishning lingvistik asoslari: Filologiya fanlari doktori (DSc) dissertatsiyasi avtoreferati. – Andijon, 2026. – 69 p.
12. Nešpore-Bērzkalne G. et al. ARANEUM corpora project documentation. – 2018.
13. “O'zbek tilining ta'limiy korpusi” [Uzbek Educational Corpus]. Electronic resource. Available at: https://uzbekcorpus.uz
14. Asadov T. Leksik-semantik guruhlarning tasnifi. – Toshkent: Fan, 2015. – 214 p.
Downloads
Details
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
