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Mathematical Formulation of Fuzzy Grammar in English Syntax and Morphology |
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PP: 1049-1065 |
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doi:10.18576/amis/190507
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Author(s) |
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Yogeesh N.,
Suleiman Ibrahim Shelash Mohammad,
N. Raja,
R. Rayeesul Hassan S.,
Kavitha H. S.,
Aravinda Reddy N.,
Asokan Vasudevan,
Mohammad Faleh Ahmmad Hunitie,
Nawaf Alshdaifat,
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Abstract |
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| This study explores the application of fuzzy set theory in the analysis of English syntax and morphology, with a focus on handling linguistic ambiguity and grammatical correctness. Traditional grammar models rely on Boolean logic, which rigidly classifies sentences as either correct or incorrect, failing to account for gradations of correctness. In contrast, fuzzy grammar models utilize membership functions to represent linguistic variability, enabling a continuous evaluation of grammatical structures. A fuzzy- based approach was applied to 10 sentences from Shakespearean literature, employing Gaussian membership functions to quantify morphological correctness. The transformation distance of each sentence was computed using Levenshtein edit distance and part- of-speech (POS) mismatches, forming the basis for fuzzy morphology analysis. The results demonstrated that modern grammatical structures achieved high fuzzy scores (μ = 1.0), whereas sentences with minor structural deviations obtained moderate scores (μ ≈ 0.9), and significant archaic variations resulted in lower scores (μ ≈ 0.6). This study highlights the effectiveness of fuzzy logic in natural language processing (NLP), particularly in context-aware grammar checking and syntactic ambiguity resolution. However, challenges remain in defining optimal membership functions and optimizing computational efficiency for real-time applications. Future research should focus on extending fuzzy grammar models to discourse analysis, integrating fuzzy neural networks for automated grammar learning, and developing hybrid AI-fuzzy grammar checking systems to enhance context-sensitive language processing. |
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