Skill: Regex vs LLM for Structured Text Parsing

Pattern Recognition

When parsing structured text (e.g., quiz questions, forms, documents):

1. Start with Regex - For well-defined patterns, regex achieves 98%+ accuracy
2. Add Confidence Scoring - Identify low-confidence extractions programmatically
3. Use LLM only for edge cases - Reserve expensive LLM calls for <5% of data

Proven Metrics (G検定 Quiz Parser)

Metric	Value
Total Questions	410
Regex Success Rate	98.0%
Low Confidence (<0.95)	8 (2.0%)
LLM Required	~5 questions
Test Coverage	93%

Architecture Pattern

Source Text
    ↓
[Regex Parser] ─── 100% structure extraction
    ↓
[Text Cleaner] ─── Remove noise (markers, page numbers)
    ↓
[Confidence Scorer] ─── Flag low-confidence items
    │
    ├── High (≥0.95) → Direct output
    │
    └── Low (<0.95) → [LLM Validator] → Output

Text Cleaning Rules Learned

Pattern	Action
（解 問答 題）	Remove (section marker)
第N章 in text	Remove (chapter reference)
[解答Nを参照]	Remove (footnote)
Stray page numbers (心理効 17 果)	Remove
（A）（B） etc.	Add newline before

Key Insights

1. Confidence Flags:

   • few_choices: Less than 4 choices extracted
   • missing_answer: No correct answer found
   • short_explanation: Explanation too brief (<50 chars)

2. TDD Approach worked well:

   • Write tests first (RED)
   • Implement minimal code (GREEN)
   • Achieved 90%+ coverage

3. Immutability Pattern:

   • Never mutate Question objects
   • Return new instances from clean_question()

When to Apply This Skill

• Quiz/exam question parsing
• Form data extraction
• Document structure parsing
• Any structured text with repeating patterns

Answer Validation Pattern

For verifying answer consistency between explanation and declared answer:

from answer_validator import generate_inconsistency_report

report = generate_inconsistency_report(questions)
# Valid: Explanation matches answer
# Invalid: Explanation suggests different answer
# Uncertain: Cannot determine from explanation

Patterns detected:

• 正解はX / 答えはX (explicit answer declaration)
• Xが正解 / Xが正しい (answer assertion)
• Xが適切 (appropriate choice)

Label normalization: ア→A, イ→B, ウ→C, エ→D

Files Created

Scripts/
├── text_cleaner.py          # Text cleaning functions
├── confidence_scorer.py     # Confidence scoring
├── llm_validator.py         # LLM validation (mock-ready)
├── hybrid_parser.py         # Integration pipeline
├── answer_validator.py      # Answer consistency checking
└── tests/
    ├── test_text_cleaner.py
    ├── test_confidence_scorer.py
    ├── test_llm_validator.py
    ├── test_hybrid_parser.py
    └── test_answer_validator.py

Usage Example

from question_parser import parse_questions_file
from text_cleaner import clean_question
from confidence_scorer import identify_low_confidence_questions

# Parse
result = parse_questions_file(source_path)

# Clean
cleaned = [clean_question(q) for q in result.questions]

# Identify issues
low_conf = identify_low_confidence_questions(cleaned)
print(f"Need review: {len(low_conf)} questions")

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Created: 2026-02-05
Project: g-kentei-ios-hybrid
Session: TDD Hybrid Parser Implementation