ronantakizawa/aozora-text-difficulty

Aozora Text Difficulty Dataset

This dataset contains Japanese literary texts from the Aozora Bunko 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: jReadability-based difficulty scoring using research-backed Japanese readability models
• Primary Methodology: 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

Total Records: 5,000 Japanese texts Total Columns: 21 Column Categories: Original Data (3) + Core Difficulty Scores (6) + Detailed Metrics (11) + Legacy Score (1)

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🗂️ Column Descriptions

Original Data Columns (3)

Column	Type	Description
text	string	Full Japanese text content from Aozora Bunko (50-532,561 characters)
footnote	string	Publishing information and bibliographic details in Japanese
meta	string	JSON metadata with work ID, title, author, and readings

Core Difficulty Scores (6) - Main Features for Learning

Column	Type	Range	Description
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
sentence_complexity	float64	0.0-1.0	Sentence length and structure variation
difficulty_level	string	categorical	Curriculum classification: Beginner/Elementary/Intermediate/Advanced/Expert

Detailed Linguistic Metrics (11)

Column	Type	Description
text_length	int64	Total character count including punctuation
kanji_density	float64	Proportion of Chinese characters (0.0-1.0)
avg_sentence_length	float64	Average characters per sentence
joyo_grade_avg	float64	Average educational grade of kanji used (1-9 scale)
lexical_diversity	float64	Unique words ÷ total words (vocabulary richness)
non_joyo_percentage	float64	Proportion of advanced kanji beyond standard education
avg_word_length	float64	Average characters per word
katakana_percentage	float64	Proportion of katakana (foreign/technical words)
word_frequency_score	float64	Vocabulary rarity score (0=common, 1=rare)
sentence_length_variance	float64	Statistical variance in sentence lengths
grammar_complexity_score	float64	Grammatical pattern complexity score

Legacy Compatibility (1)

Column	Type	Range	Description
difficulty_score	float64	0.0-10.0	Traditional 10-point difficulty scale for compatibility

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🎯 Difficulty Calculation Methodology

Primary Difficulty Score: jReadability Model

The overall_difficulty score is calculated using the jReadability Python library, which implements the research-backed Japanese readability model developed by Jae-ho Lee and Yoichiro Hasebe.

jReadability Model Formula:

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
• Intermediate (0.35-0.54): Standard literary works
• Advanced (0.55-0.74): Complex literary language
• Expert (0.75-1.00): Classical or highly sophisticated texts

Supporting Linguistic Metrics

1. Kanji Analysis: 3,003 kanji with official educational grades from kanjiapi.dev
2. Vocabulary Analysis: 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)

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