A Mathematical Fuzzy Model for Syntax-Pragmatics Interface

This study proposes a novel fuzzy grammar model to analyze the syntax-pragmatics interface by integrating fuzzy logic into linguistic evaluation. Traditional binary models of grammaticality fail to capture the graded acceptability observed in natural language, where subtle variations in syntactic structure and contextual cues interact to determine overall language performance. Our approach normalizes Likert-scale ratings of syntactic well-formedness and pragmatic appropriateness into fuzzy membership values, enabling a continuous representation of linguistic acceptability. The model employs fuzzy membership functions—primarily using linear normalization—and aggregates syntactic and pragmatic scores using the minimum operator to reflect the principle that a sentence is as acceptable as its weakest component. A small experimental dataset comprising five sentences was used to illustrate the model’s implementation, where descriptive statistics, visual bar charts, and fuzzy inference outputs demonstrated that sentences with inconsistent syntactic and pragmatic ratings yield lower overall acceptability. The results underscore fuzzy logic’s efficacy in distinguishing borderline cases and capturing the nuanced interplay between formal structure and context-sensitive meaning. This integrative framework not only extends theoretical insight into language processing but also offers promising applications in natural language processing, language education, and cross-linguistic studies. Future research should empirically validate the model with larger datasets and explore alternative fuzzy aggregation strategies.