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README.md

@@ -18,7 +18,7 @@ configs:
 - config_name: default
   data_files:
   - split: train
-    path: "aozorabunko_with_difficulty_5k_clean.csv"
+    path: "aozorabunko_with_jreadability_5k.csv"
 dataset_info:
   features:
   - name: text
@@ -70,13 +70,14 @@ dataset_info:
 
 # Aozora Text Difficulty Dataset
 
-This dataset contains Japanese literary texts from the [Aozora Bunko](https://www.aozora.gr.jp/) digital library, enhanced with comprehensive difficulty analysis scores for Japanese language learning and curriculum development.
+This dataset contains Japanese literary texts from the [Aozora Bunko](https://www.aozora.gr.jp/) digital library, enhanced with **jReadability-based difficulty analysis** for Japanese language learning and curriculum development.
 
 ## Dataset Overview
 
 - **Source**: Aozora Bunko (青空文庫) - Japan's premier digital library of public domain literature
-- **Enhancement**: Comprehensive linguistic difficulty analysis using authentic Japanese language resources
-- **Use Cases**: Japanese language curriculum design, reading level assessment, adaptive learning systems
+- **Enhancement**: jReadability-based difficulty scoring using research-backed Japanese readability models
+- **Primary Methodology**: [jReadability](https://github.com/joshdavham/jreadability) - A Python implementation of Lee & Hasebe's Japanese readability evaluation system
+- **Use Cases**: Japanese language curriculum design, reading level assessment, adaptive learning systems, difficulty-controlled text generation
 - **License**: Original Aozora Bunko texts are public domain; analysis code and scores are provided under open source terms
 
 ## 📊 Dataset Structure
@@ -101,7 +102,7 @@ This dataset contains Japanese literary texts from the [Aozora Bunko](https://ww
 
 | Column | Type | Range | Description |
 |--------|------|-------|-------------|
-| **`overall_difficulty`** | float64 | 0.0-1.0 | **Primary difficulty score** combining all linguistic factors |
+| **`overall_difficulty`** | float64 | 0.0-1.0 | **Primary difficulty score** based on jReadability model |
 | **`kanji_difficulty`** | float64 | 0.0-1.0 | Complexity based on kanji grade levels and density |
 | **`lexical_difficulty`** | float64 | 0.0-1.0 | Vocabulary complexity using authentic frequency data |
 | **`grammar_complexity`** | float64 | 0.0-1.0 | Grammatical structure complexity |
@@ -134,16 +135,24 @@ This dataset contains Japanese literary texts from the [Aozora Bunko](https://ww
 
 ## 🎯 Difficulty Calculation Methodology
 
-### **Overall Difficulty Formula**
+### **Primary Difficulty Score: jReadability Model**
+
+The `overall_difficulty` score is calculated using the [jReadability](https://github.com/joshdavham/jreadability) Python library, which implements the research-backed Japanese readability model developed by **Jae-ho Lee and Yoichiro Hasebe**.
+
+**jReadability Model Formula:**
 ```
-overall_difficulty = (
-    0.30 × kanji_difficulty +      # Kanji complexity (30%)
-    0.25 × lexical_difficulty +    # Vocabulary difficulty (25%)
-    0.25 × grammar_complexity +    # Grammar complexity (25%)
-    0.20 × sentence_complexity     # Sentence structure (20%)
-)
+readability = {mean words per sentence} × -0.056
+            + {percentage of kango} × -0.126      # Chinese-origin words
+            + {percentage of wago} × -0.042       # Native Japanese words
+            + {percentage of verbs} × -0.145
+            + {percentage of particles} × -0.044
+            + 11.724
 ```
 
+**Score Normalization:**
+- jReadability output: 0.5-6.5 (higher = easier)
+- Our normalization: `(6.5 - jreadability_score) / 6.0` → 0-1 scale (higher = harder)
+
 ### **Curriculum Level Classification**
 - **Beginner** (0.00-0.19): Basic modern Japanese
 - **Elementary** (0.20-0.34): Simple literary texts
@@ -151,20 +160,52 @@ overall_difficulty = (
 - **Advanced** (0.55-0.74): Complex literary language
 - **Expert** (0.75-1.00): Classical or highly sophisticated texts
 
-### **Authentic Data Sources**
-1. **Kanji Grades**: 3,003 kanji with official educational grades from [kanjiapi.dev](https://kanjiapi.dev)
-2. **Word Frequencies**: [wordfreq](https://github.com/rspeer/wordfreq) library with real corpus data
+### **Supporting Linguistic Metrics**
+1. **Kanji Analysis**: 3,003 kanji with official educational grades from [kanjiapi.dev](https://kanjiapi.dev)
+2. **Vocabulary Analysis**: [wordfreq](https://github.com/rspeer/wordfreq) library with real corpus data
 3. **Grammar Analysis**: Pattern-based complexity scoring using formal Japanese constructions
+4. **Sentence Analysis**: Length variation and structural complexity measures
+
+### **Research Foundation**
+- **Lee, J. & Hasebe, Y.** *Introducing a readability evaluation system for Japanese language education*
+- **Lee, J. & Hasebe, Y.** *Readability measurement of Japanese texts based on levelled corpora*
+- Model specifically designed for **non-native Japanese learners** (not native speaker grade levels)
 
 ---
 
 ## 📈 Dataset Statistics
 
+**jReadability-Based Analysis Results (5,000 texts):**
+- **Overall Difficulty**: Mean 0.547 (0.0-1.0 scale)
+- **Difficulty Distribution**:
+  - Beginner: 97 texts (1.9%)
+  - Elementary: 552 texts (11.0%)
+  - Intermediate: 2,047 texts (40.9%)
+  - Advanced: 1,690 texts (33.8%)
+  - Expert: 614 texts (12.3%)
+
+**Text Characteristics:**
 - **Text Length**: 50 - 532,561 characters (mean: ~11,285)
 - **Kanji Density**: 29.4% average
 - **Average Joyo Grade**: 3.71 (elementary-intermediate level)
 - **Lexical Diversity**: 0.270 (moderate vocabulary variation)
-- **Difficulty Distribution**: 18.3% Elementary, 81.7% Intermediate
+
+---
+
+## 🔬 jReadability Advantages
+
+### **Why jReadability?**
+1. **Research-Backed**: Based on empirical studies of Japanese learner corpora
+2. **Learner-Focused**: Designed specifically for non-native Japanese speakers
+3. **Linguistic Sophistication**: Considers Japanese-specific features (kango/wago ratios, particle usage)
+4. **Reproducible**: Standardized implementation with consistent results
+5. **Validated**: Published research with proven correlation to learner difficulty perception
+
+### **Improvements Over Composite Scoring**
+- **Holistic Assessment**: Considers text as a unified linguistic entity rather than separate features
+- **Native Speaker Bias Reduction**: Avoids assumptions based on native speaker intuitions
+- **Empirical Foundation**: Based on actual learner performance data
+- **Standardized Scale**: Consistent 6-level difficulty assessment widely used in Japanese education
 
 ---
 
@@ -215,16 +256,54 @@ elementary_kanji = df[df['joyo_grade_avg'] <= 4.0]
 ## 🎓 Applications
 
 - **Language Learning**: Personalized reading recommendations based on learner level
-- **Curriculum Design**: Structured progression of reading materials
-- **Assessment Tools**: Automatic text difficulty evaluation for placement
-- **Research**: Japanese language complexity and readability analysis
+- **Curriculum Design**: Structured progression of reading materials using research-backed difficulty assessment
+- **Assessment Tools**: Automatic text difficulty evaluation for placement using jReadability standards
+- **Research**: Japanese language complexity and readability analysis with validated metrics
 - **EdTech**: Adaptive learning system development and content curation
+- **Text Generation**: Difficulty-controlled generation of Japanese educational content
+- **Reading Comprehension**: Graded text selection for language learning platforms
+
+---
+
+## 🛠️ Technical Implementation
+
+### **jReadability Integration**
+```python
+from jreadability import compute_readability
+
+# Calculate jReadability score
+jreadability_score = compute_readability(japanese_text)
+
+# Normalize to 0-1 difficulty scale (0=easiest, 1=hardest)
+overall_difficulty = max(0.0, min(1.0, (6.5 - jreadability_score) / 6.0))
+```
+
+### **Batch Processing**
+For optimal performance when processing large datasets:
+```python
+from fugashi import Tagger
+from jreadability import compute_readability
+
+# Initialize tagger once for batch processing
+tagger = Tagger()
+
+# Process multiple texts efficiently
+for text in texts:
+    score = compute_readability(text, tagger)  # Reuse tagger
+```
+
+### **Dependencies**
+- **jreadability**: Research-backed Japanese readability calculation
+- **fugashi**: Fast Japanese morphological analysis (MeCab wrapper)
+- **unidic-lite**: Japanese linguistic resources
+- **wordfreq**: Authentic Japanese word frequency data
 
 ---
 
 ## 🔗 Related Datasets
 
 - [Japanese Character Difficulty Dataset](https://huggingface.co/datasets/ronantakizawa/japanese-character-difficulty) - Kanji grades used in this analysis
+- [jReadability GitHub](https://github.com/joshdavham/jreadability) - Original jReadability implementation
 
 ---