Linguistic Analysis of Japanese Text Simplification and Implications for AI-Driven Educational Tools
Why this work is in the frame
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Bibliographic record
Abstract
This study examines the SNOW Simplified Japanese Corpora (T15 and T23) to analyze how text simplification transforms linguistic features across Japanese and English, intending to inform AI-driven educational tools. Applying POS tagging, dependency parsing, and named-entity recognition to parallel texts, we identify distinctive patterns including increased noun usage and reduced determiner frequency in simplified Japanese. This analysis reveals that simplified Japanese texts demonstrate higher noun density, fewer determiners, and selective preservation of named entities compared to their English counterparts. These findings suggest that simplification reduces language learners’ cognitive load by anchoring discourse in more concrete concepts. The linguistic patterns identified can inform the development of adaptive AI-powered learning systems that effectively balance accessibility with authentic language exposure and nuance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it