This study introduces Linguistic Su-Field Engineering, a TRIZ-based approach for diagnosing and repairing semantic inconsistencies in Turkish sentences. By applying Su-Field logic to natural language, the study proposes a functional bridge between engineering problem solving and linguistic clarity enhancement.
Turkish sentences were modeled as functional systems composed of subject (S1), action (F) and object (S2), reflecting Su-Field structures rather than Turkish SOV syntax. Thirty-five linguistic Su-Field patterns were derived from common ambiguity types and paired with relevant TRIZ Standard Solutions. A quasi-experimental study with 20 non-native learners compared comprehension of original versus TRIZ-repaired text passages. Improvements were evaluated using the Wilcoxon signed-rank test.
TRIZ-repaired sentences yielded statistically significant gains in comprehension and perceived clarity. Learners reported that the explicit functional roles and clarified interactions reduced cognitive load and improved interpretability. The results indicate that TRIZ-based semantic repair enhances sentence-level coherence and instructional usefulness.
Although evaluated on Turkish, the method is applicable to other morphologically rich and structurally flexible languages, providing a conceptual foundation for future rule-based and interpretable NLP tools.
To the best of the authors knowledge, the study is the first to integrate Su-Field logic into natural language systems, offering a transparent and interpretable mechanism for semantic repair. It extends TRIZ beyond engineering applications toward linguistic analysis and explainable NLP.
