FUZZY LOGIC AND THE INTERACTION OF LEXICAL SYNONYMS IN NATURAL LANGUAGE
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Abstract
Fuzzy logic, while extensively applied in technical fields such as mathematics, engineering, and computer science, also provides profound insights into the structure and functioning of natural language. This article explores the intersection of fuzzy logic and lexical synonymy, focusing on how imprecise linguistic expressions reflect cognitive categorization and contextual variation. By examining the theoretical underpinnings laid out by Lotfi A. Zadeh and subsequent developments in fuzzy linguistic modeling, this study demonstrates the practical utility of fuzzy logic in modeling semantic gradation, contextual meaning shifts, and synonym selection in natural language. Applications in natural language processing (NLP), sentiment analysis, and artificial intelligence show how fuzzy logic enhances the interpretability of synonymous expressions and mirrors the way humans use language in real-world scenarios. The discussion also highlights the role of fuzzy sets in capturing lexical nuance, the benefits of degree-based semantic modeling, and the integration of fuzzy logic with modern computational tools, offering a comprehensive framework for more human-aligned language technologies.
How to Cite
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Fuzzy logic, lexical synonymy, semantic gradation, natural language processing (NLP), cognitive categorization, fuzzy linguistic modeling, contextual meaning
2. Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59, 617–645. https://doi.org/10.1146/annurev.psych.59.103006.093639
3. Cambria, E., Poria, S., Gelbukh, A., & Thelwall, M. (2020). Sentiment analysis is a big suitcase. IEEE Intelligent Systems, 35(3), 43–49. https://doi.org/10.1109/MIS.2020.2971449
4. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), 4171–4186. https://doi.org/10.48550/arXiv.1810.04805
5. Lakoff, G. (1987). Women, fire, and dangerous things: What categories reveal about the mind. University of Chicago Press.
6. Ostrovski, L. A. (1999). Kognitivnaya semantika i teoriya smyslov [Cognitive semantics and the theory of meanings]. Nauka.
7. Rosch, E. (1975). Cognitive representations of semantic categories. Journal of Experimental Psychology: General, 104(3), 192–233.
8. Ullmann, S. (1962). Semantics: An introduction to the science of meaning. Blackwell.
9. Yager, R. R., & Zadeh, L. A. (Eds.). (1990). The management of uncertainty in knowledge-based systems. Springer.
10. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
11. Zadeh, L. A. (1996). Fuzzy logic = computing with words. IEEE Transactions on Fuzzy Systems, 4(2), 103–111. https://doi.org/10.1109/91.493904