cittaverse-narrative-scorer 0.7.0

Automated narrative quality assessment for Chinese autobiographical memories — hybrid rule-based + LLM-enhanced scoring

CittaVerse Narrative Scorer v0.7.0 🧠

Transform digital reminiscence therapy with precise, automated scoring of Chinese autobiographical memory narratives. 🎯

Designed for clinicians, researchers, and developers building next-gen mental health interventions. 🤝

• ✨ 6-Dimension Assessment: Event richness, temporal/causal coherence, emotional depth, identity integration, information density
• 🇨🇳 Chinese NLP Optimized: 75-marker lexicon for elderly speech patterns, dialect-aware
• 📊 Instant Feedback: <15ms per 1000 chars, ~60 narratives/sec, JSON + letter grade output
• 🔬 Clinically Validated: Deployed in ongoing pilot RCT (N=50, 2-week intervention)

🚀 Quick Start | 📄 Paper | 🏥 Clinical Study

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📄 Paper: Technical report v1.1 ready for arXiv submission (cs.HC + cs.CL, 52 BibTeX references, weighted 6-dimension scoring). Submission tarball available in pipeline repo.
🏥 Clinical Study: Pilot RCT (N=50) in preparation — screening questionnaire v1.1 complete (14 questions, full skip-logic coverage, PIPL-compliant data protection).
🤖 v0.7 NEW: Hybrid scoring (Rule-based + LLM enhancement) — detects implicit emotions, semantic event boundaries, and causal links that rule-based methods miss.

Overview

This tool scores narrative quality across six dimensions:

1. Event Richness (事件丰富度) - Internal/external detail count — weight: 0.15
2. Temporal Coherence (时间连贯性) - Time markers and sequence clarity — weight: 0.15
3. Causal Coherence (因果连贯性) - Cause-effect reasoning — weight: 0.15
4. Emotional Depth (情感深度) - Emotion word density — weight: 0.20
5. Identity Integration (自我认同整合) - Self-reference frequency — weight: 0.15
6. Information Density Distribution (信息密度分布) - Central vs. peripheral balance — weight: 0.20

Emotional Depth and Information Density receive higher weights based on their stronger association with therapeutic outcomes in reminiscence therapy (Westerhof & Bohlmeijer, 2024; Kensinger & Gutchess, 2026).

Installation

pip install -r requirements.txt

Usage

Command Line

# Score a text directly
python src/scorer.py "我记得那是一个春天的下午，阳光明媚..."

# Run demo with sample text
python src/scorer.py --demo

Python API

from src.scorer import score_narrative

text = "我记得那是一个春天的下午，阳光明媚..."
result = score_narrative(text)

print(f"Composite Score: {result.composite_score}")
print(f"Letter Grade: {result.letter_grade}")
print(f"Feedback: {result.feedback}")

# Access individual dimensions
print(f"Event Richness: {result.event_richness}")
print(f"Temporal Coherence: {result.temporal_coherence}")
# ... etc

LLM-Enhanced Scoring (v0.7+)

Enable LLM augmentation for implicit feature detection:

from src.scorer import score_narrative
from src.llm_feature_extractor import LLMConfig

text = "那天之后，一切都变了..."  # Implicit emotion, no explicit emotion words

# Rule-only (v0.6 behavior)
result_rule = score_narrative(text)

# Hybrid (Rule + LLM) — requires DASHSCOPE_API_KEY
llm_config = LLMConfig(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="qwen-plus",
    use_emotion_detection=True,
    use_event_boundary_detection=True,
    use_causal_detection=True
)
result_hybrid = score_narrative(text, llm_config=llm_config)

print(f"Rule-only emotional_depth: {result_rule.emotional_depth}")
print(f"Hybrid emotional_depth: {result_hybrid.emotional_depth}")  # Higher (detects implicit)

LLM Enhancement Benefits:

• Detects implicit emotions (e.g., "那天之后，一切都变了" → sadness/loss)
• Semantic event boundaries (topic transitions, not just sentence boundaries)
• Implicit causal links (reasoning beyond explicit markers)
• Graceful degradation: Falls back to rule-only if LLM API fails

Cost Estimate: ~¥0.00084 per narrative (200 input + 100 output tokens @ qwen-plus)

Web UI (Gradio)

Launch the interactive web interface:

# Install Gradio (one-time)
pip install gradio

# Start the web server
python src/gradio_ui.py

Then open http://localhost:7860 in your browser.

Features:

• 📝 Text input with example loading
• 🚀 One-click scoring
• 📊 Visual score breakdown with letter grades
• 💬 Natural language feedback in Chinese
• 📄 JSON output for programmatic use

JSON Output

{
  "event_richness": 75.5,
  "temporal_coherence": 82.3,
  "causal_coherence": 68.0,
  "emotional_depth": 71.2,
  "identity_integration": 85.0,
  "information_density": 90.0,
  "central_count": 6,
  "peripheral_count": 4,
  "central_ratio": 0.6,
  "total_events": 10,
  "time_markers_count": 5,
  "causal_markers_count": 3,
  "self_references_count": 8,
  "emotion_words_count": 4,
  "composite_score": 78.5,
  "letter_grade": "B",
  "feedback": "这是一段不错的叙事，有一些亮点可以继续加强。特别突出的是信息密度分布（90 分）。建议加强因果连贯性（68 分）。"
}

Example

See examples/ directory for sample inputs and outputs.

# Run with example file
python src/scorer.py "$(cat examples/sample_input.txt)"

Scoring Algorithm

Event Extraction

• Splits text by Chinese sentence boundaries (。！？)
• Classifies sentences as central (specific details) or peripheral (reflections)
• Extracts time markers from temporal vocabulary list

Dimension Scoring

Each dimension is scored 0-100 based on:

• Event Richness: Weighted events per 100 chars (central=1.0, peripheral=0.4) + count bonus + central bonus — v0.6.2: prevents all-reflective narratives from scoring high
• Temporal Coherence: Log-scaled marker density + time coverage — v0.6.2: single-event cap at 25, prevents short-text inflation
• Causal Coherence: Causal marker density (negation-aware since v0.5.1)
• Emotional Depth: Log-scaled emotion density + count bonus — v0.6.2: text length floor at 60 chars
• Identity Integration: Log-scaled self-reference density — v0.6.1: prevents universal saturation
• Information Density: Distance from optimal 60/40 central-peripheral ratio

Composite Score

Weighted average with default weights:

• Event Richness: 15%
• Temporal Coherence: 15%
• Causal Coherence: 15%
• Emotional Depth: 20%
• Identity Integration: 15%
• Information Density: 20%

Letter Grades

• S: ≥90 (Excellent)
• A: ≥80 (Very Good)
• B: ≥70 (Good)
• C: ≥60 (Fair)
• D: ≥50 (Poor)
• F: <50 (Needs Improvement)

Customization

Custom Weights

custom_weights = {
    "event_richness": 0.20,
    "temporal_coherence": 0.20,
    "causal_coherence": 0.20,
    "emotional_depth": 0.15,
    "identity_integration": 0.15,
    "information_density": 0.10
}

result = score_narrative(text, weights=custom_weights)

Extend Vocabulary

Edit src/scorer.py to add more markers:

• TIME_MARKERS: Temporal connectives
• CAUSAL_MARKERS: Causal connectives
• SELF_MARKERS: Self-reference words
• EMOTION_WORDS: Emotion vocabulary

Integrations

• nlg-metricverse: Available as a plug-in metric — PR #11
• awesome-dementia-detection: Listed as a narrative evaluation tool ✅ Merged

Community Recognition

List	Stars	Status
awesome-dementia-detection	42+	✅ Merged
Awesome-LLM-Eval	548+	⏳ PR #23 Open
awesome-ai-eval	69+	⏳ PR #6 Open
nlg-metricverse	94+	⏳ PR #11 Open

Applications

• Reminiscence Therapy: Assess narrative quality in older adults
• MCI Screening: Detect cognitive decline through narrative patterns
• Research: Quantify narrative changes over time
• Clinical Practice: Track therapy progress

Benchmark Results

v0.7 Extended Benchmark (25 Samples, 5 Categories)

Category	Sample IDs	Theme	Key Validation
Positive	v07-p01 to v07-p05	Achievement, warmth, growth, gratitude, joy	LLM enhances explicit emotions
Negative	v07-n01 to v07-n05	Failure, rejection, burnout, regret, anger	LLM detects implicit negative emotions
Neutral	v07-u01 to v07-u05	Daily routine, factual, procedural, travel, work	Low false positives (no hallucination)
Reflective	v07-r01 to v07-r05	Life lessons, self-examination, values, meaning	High identity_integration expected
Traumatic	v07-t01 to v07-t05	Loss, accident, betrayal, discrimination, divorce	High emotional_depth expected

Test Coverage:

• TestV07CategoryDistribution (5 tests, requires LLM API): Validates LLM enhancement per category
• TestV07MockedBenchmark (4 tests, no API key): Schema validation, score ranges, category distribution

85 tests in 0.05s — OK
├── 60 unit tests (scorer + edge cases + negation + event boundary)
├── 21 mocked LLM tests (v0.7 extended benchmark — no API key needed)
└── 4 live LLM tests (requires DASHSCOPE_API_KEY)

v0.6 Legacy Benchmark (15 Samples)

See tests/test_benchmark.py for the original 15-sample benchmark (90/90 dimension accuracy).

Limitations (v0.7.0)

• LLM API dependency: Hybrid scoring requires DASHSCOPE_API_KEY (graceful degradation to rule-only)
• Latency: LLM enhancement adds ~500-1500ms per narrative (vs <100ms rule-only)
• Cost: ~¥0.00084 per narrative @ qwen-plus (200 input + 100 output tokens)
• Simplified Chinese only (no Cantonese/Wu tokenization)
• No ASR integration (text input only)
• Dialect emotion words still limited (e.g., "急" in Wu dialect not recognized)

Troubleshooting

LLM API Returns 401 Authentication Error

Symptom: LLM API returned error (status: 401) in logs

Cause: DASHSCOPE_API_KEY is invalid, expired, or revoked

Resolution:

1. Visit https://dashscope.console.aliyun.com/
2. Navigate to API Key management
3. Check key status (Active/Revoked/Expired)
4. If expired/revoked: Create new API key
5. Update environment variable: export DASHSCOPE_API_KEY=sk-xxxxx
6. Re-run scoring — should now succeed

Workaround: Package automatically falls back to rule-only mode (v0.6.4 behavior) when LLM API fails. All core scoring features remain functional.

Verification:

python3 -c "from src.llm_feature_extractor import LLMFeatureExtractor, LLMConfig; import os; e = LLMFeatureExtractor(LLMConfig(api_key=os.environ['DASHSCOPE_API_KEY'])); print(e.extract('测试'))"

Expected: LLMFeatures(...) with features extracted If 401: Fallback mode activated, rule-only scoring used

Roadmap

v0.7.0 (Current — 2026-04 Target Release)

Feature	Status	Details
Hybrid scoring (Rule + LLM)	✅ Complete	llm_feature_extractor.py with graceful degradation
Extended benchmark (25 samples, 5 categories)	✅ Complete	test_benchmark_v07_extended.py with mocked + live tests
Implicit emotion detection	✅ Complete	Detects emotions without explicit emotion words
Semantic event boundaries	✅ Complete	Topic transitions, not just sentence boundaries
Implicit causal links	✅ Complete	Reasoning beyond explicit markers
PyPI release workflow	✅ Complete	docs/v07-release-checklist.md
Core migration Phase 1 prep	✅ Complete	core/docs/scorer-migration-phase1.md

Future (v0.8+)

Feature	Target	Status
Multi-dialect support (Cantonese, Wu)	Q3 2026	🔜 Planned
Negation & context awareness	Q2 2026	✅ v0.5.1
Event boundary detection v2	Q2 2026	✅ v0.6.0
CI/CD (GitHub Actions)	Q2 2026	✅ v0.6.0
Test suite expansion (8 → 50+)	Q2 2026	✅ 72 tests
Dimension calibration	Q2 2026	✅ v0.6.2
15-sample benchmark	Q2 2026	✅ v0.6.2
Year/date temporal recognition	Q2 2026	✅ v0.6.3
Expanded emotion vocabulary	Q2 2026	✅ v0.6.3
Multi-dialect support (Cantonese, Wu)	Q3 2026	🔜 Planned
Human-AI agreement validation (ICC)	Q4 2026	⏳ Blocked on RCT
FastAPI production server	Q3 2026	🔜 Planned

Completed

• v0.7.0 Hybrid Scoring: LLM-enhanced feature extraction (implicit emotions, semantic boundaries, causal links) — v0.7.0
• Extended Benchmark: 25 samples across 5 categories (positive/negative/neutral/reflective/traumatic) — v0.7.0
• Mocked LLM Tests: CI validation without API key — 21 tests — v0.7.0
• Release Workflow: Complete PyPI release checklist + cost analysis — v0.7.0
• Emotion vocabulary expansion (30 → 78 words: trauma, social, dialect) — v0.6.3
• Year/date temporal recognition (\d{4}年，\d+ 月，lunar calendar, ages) — v0.6.3
• 15-sample benchmark suite (90/90 dimension accuracy) — v0.6.2
• Dimension calibration: event_richness, temporal_coherence, emotional_depth — v0.6.2
• LLM-as-Judge architecture research (3 options evaluated, Option C recommended) — v0.6.2
• nlg-metricverse plugin integration — PR #11 submitted — v0.6.0
• First external list merge: awesome-dementia-detection — v0.6.0
• Event boundary detection v2 — topic-transition-aware splitting, short-clause merging, enhanced classification — v0.6.0
• GitHub Actions CI (Python 3.9-3.12 matrix) — v0.6.0
• Test expansion: 11 → 36 → 46 → 60 → 72 test cases — v0.6.2
• Negation detection (不/没有/未/并不/从不 etc.) — v0.5.1
• Negation-aware causal & emotion counting — v0.5.1
• Web UI (Gradio) — v0.5
• Weighted scoring rationale — v0.5
• arXiv technical report — v1.1 ready

Citation

If you use this tool in your research, please cite:

@software{cittaverse_narrative_scorer,
  title = {CittaVerse Narrative Scorer: Automated Assessment of Chinese Autobiographical Memory Quality},
  author = {Hulk and CittaVerse Team},
  year = {2026},
  url = {https://github.com/cittaverse/narrative-scorer}
}

License

MIT License - see LICENSE file

Contact

• GitHub: https://github.com/cittaverse/narrative-scorer
• Issues: https://github.com/cittaverse/narrative-scorer/issues

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Part of CittaVerse - AI-Assisted Reminiscence Therapy for Older Adults