SENTENCE_LEVEL_CATEGORIZATION_PLAN.md

📋 Sentence-Level Categorization - ✅ IMPLEMENTED

Status: ✅ COMPLETE - All 7 phases implemented and deployed

Problem Identified: Single submissions often contain multiple semantic units (sentences) belonging to different categories, leading to loss of nuance.

Example:

"Dallas should establish more green spaces in South Dallas neighborhoods. Areas like Oak Cliff lack accessible parks compared to North Dallas."

• Sentence 1: Objectives (should establish...)
• Sentence 2: Problem (lack accessible parks...)

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✅ Implementation Status

Phase 1: Database Schema ✅ COMPLETE

• ✅ SubmissionSentence model created
• ✅ sentence_analysis_done flag added to Submission
• ✅ sentence_id foreign key added to TrainingExample
• ✅ Helper methods: get_primary_category(), get_category_distribution()
• ✅ Database migration script completed

Files:

• app/models/models.py (lines 85-114): SubmissionSentence model
• app/models/models.py (lines 34-60): Updated Submission model
• migrations/migrate_to_sentence_level.py: Migration script

Phase 2: Sentence Segmentation ✅ COMPLETE

• ✅ Rule-based sentence segmenter created
• ✅ Handles abbreviations (Dr., Mr., etc.)
• ✅ Handles bullet points and special punctuation
• ✅ Minimum length validation

Files:

• app/sentence_segmenter.py: SentenceSegmenter class with comprehensive logic

Phase 3: Analysis Pipeline ✅ COMPLETE

• ✅ analyze_sentences() method - analyzes list of sentences
• ✅ analyze_with_sentences() method - segments and analyzes in one call
• ✅ Each sentence classified independently
• ✅ Confidence scores tracked (when available)

Files:

• app/analyzer.py (lines 282-313): analyze_sentences method
• app/analyzer.py (lines 315-332): analyze_with_sentences method

Phase 4: Backend API ✅ COMPLETE

• ✅ Analysis endpoint updated for sentence-level
• ✅ Sentence category update endpoint (/api/update-sentence-category/<id>)
• ✅ Training examples linked to sentences
• ✅ Backward compatibility maintained

Files:

• app/routes/admin.py (lines 372-429): Updated analyze endpoint
• app/routes/admin.py (lines 305-354): Sentence category update endpoint

Phase 5: UI/UX ✅ COMPLETE

• ✅ Collapsible sentence view in submissions
• ✅ Category distribution badges
• ✅ Individual sentence category dropdowns
• ✅ Real-time sentence category editing
• ✅ Visual feedback for changes

Files:

• app/templates/admin/submissions.html (lines 69-116): Sentence-level UI

Phase 6: Dashboard Aggregation ✅ COMPLETE

• ✅ Dual-mode dashboard (Submissions vs Sentences)
• ✅ Toggle button for view mode
• ✅ Sentence-based category statistics
• ✅ Contributor breakdown by sentences
• ✅ Backward compatible with submission-level

Files:

• app/routes/admin.py (lines 117-181): Updated dashboard route
• app/templates/admin/dashboard.html (lines 1-20): View mode selector

Phase 7: Migration & Testing ✅ COMPLETE

• ✅ Migration script with SQL ALTER statements
• ✅ Safely adds columns to existing tables
• ✅ 60 submissions migrated successfully
• ✅ Backward compatibility verified
• ✅ Sentence-level analysis tested and working

Files:

• migrations/migrate_to_sentence_level.py: Complete migration script

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🎯 Additional Features Implemented

Training Data Management

• ✅ Export training examples (with sentence-level filter)
• ✅ Import training examples from JSON
• ✅ Clear training examples (with safety options)
• ✅ Sentence-level training data preference

Files:

• app/routes/admin.py (lines 748-886): Export/Import/Clear endpoints
• app/templates/admin/training.html (lines 64-126): Training data management UI

Fine-Tuning Enhancements

• ✅ Sentence-level vs submission-level training toggle
• ✅ Filters training data to use only sentence-level examples
• ✅ Falls back to all examples if insufficient sentence-level data
• ✅ Detailed progress tracking (epoch/step/loss)
• ✅ Real-time progress updates during training

Files:

• app/routes/admin.py (lines 893-910): Training data filtering
• app/fine_tuning/trainer.py (lines 34-102): ProgressCallback for tracking
• app/templates/admin/training.html (lines 174-189): Sentence-level training option

Model Management

• ✅ Force delete training runs
• ✅ Bypass all safety checks for stuck runs
• ✅ Confirmation prompt requiring "DELETE" text
• ✅ Model file cleanup on deletion

Files:

• app/routes/admin.py (lines 1391-1430): Force delete endpoint
• app/templates/admin/training.html (lines 920-952): Force delete function

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📊 How It Works

1. Submission Flow

User submits text
    ↓
Stored in database
    ↓
Admin clicks "Analyze All"
    ↓
Text segmented into sentences (sentence_segmenter.py)
    ↓
Each sentence classified independently (analyzer.py)
    ↓
Results stored in submission_sentences table
    ↓
Primary category calculated from sentence distribution

2. Training Flow

Admin reviews sentences
    ↓
Corrects individual sentence categories
    ↓
Each correction creates a sentence-level training example
    ↓
Training examples exported/imported as needed
    ↓
Model trained using only sentence-level data (when enabled)
    ↓
Fine-tuned model deployed for better accuracy

3. Dashboard Aggregation

Admin selects view mode (Submissions vs Sentences)
    ↓
If Submissions: Count by primary category per submission
    ↓
If Sentences: Count all sentences by category
    ↓
Charts and statistics update accordingly

---

🎨 UI Features

Submissions Page

• View Sentences button shows count: (3) sentences
• Click to expand collapsible sentence list
• Each sentence displays:
  • Sentence number
  • Text content
  • Category dropdown (editable)
  • Confidence score (if available)
• Category distribution badges show percentages

Dashboard

• Toggle buttons: "By Submissions" | "By Sentences"
• Charts update based on selected mode
• Category breakdown shows different totals
• Contributor statistics remain submission-based

Training Page

• Checkbox: "Use Sentence-Level Training Data" (default: checked)
• Export with "Sentence-level only" filter
• Import shows sentence vs submission counts
• Clear with "Sentence-level only" option

---

🗂️ Database Schema

submission_sentences Table

CREATE TABLE submission_sentences (
    id INTEGER PRIMARY KEY,
    submission_id INTEGER NOT NULL,
    sentence_index INTEGER NOT NULL,
    text TEXT NOT NULL,
    category VARCHAR(50),
    confidence REAL,
    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
    FOREIGN KEY (submission_id) REFERENCES submissions(id),
    UNIQUE (submission_id, sentence_index)
);

Updated submissions Table

ALTER TABLE submissions
ADD COLUMN sentence_analysis_done BOOLEAN DEFAULT 0;

Updated training_examples Table

ALTER TABLE training_examples
ADD COLUMN sentence_id INTEGER REFERENCES submission_sentences(id);

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📈 Usage Statistics

Current Database (as of implementation):

• Total submissions: 60
• Sentence-level analyzed: Yes
• Total training examples: 71
  • Sentence-level: 11
  • Submission-level: 60
• Training runs: 12

---

🔧 Configuration

Enable Sentence-Level Analysis

In admin interface:

1. Go to Submissions
2. Click "Analyze All"
3. System automatically uses sentence-level (default)

Train with Sentence Data

In admin interface:

1. Go to Training
2. Check "Use Sentence-Level Training Data"
3. Click "Start Training"
4. System uses only sentence-level examples (falls back if < 20)

View Sentence Analytics

In admin interface:

1. Go to Dashboard
2. Click "By Sentences" toggle
3. Charts show sentence-based aggregation

---

🚀 Performance Notes

Sentence Segmentation: ~50-100ms per submission (rule-based, fast)

Classification: ~200-500ms per sentence (BART model, CPU)

• 3-sentence submission: ~600-1500ms total
• Can be parallelized in future

Database Queries: Optimized with indexes on foreign keys

UI Rendering: Lazy loading with Bootstrap collapse components

---

🔄 Backward Compatibility

✅ Fully backward compatible:

• Old submission.category field preserved
• Automatically set to primary category from sentences
• Legacy submissions work without re-analysis
• Dashboard supports both view modes
• Training examples support both types

---

📝 Next Steps (Future Enhancements)

Potential Improvements

1. ⏭️ Parallel sentence classification (faster bulk analysis)
2. ⏭️ Confidence threshold filtering
3. ⏭️ Sentence-level map markers (optional)
4. ⏭️ Advanced NLP: Named entity recognition
5. ⏭️ Sentence similarity clustering
6. ⏭️ Multi-language support

Optimization Opportunities

1. ⏭️ Cache sentence segmentation results
2. ⏭️ Batch sentence classification API
3. ⏭️ Database indexes on category fields
4. ⏭️ Async processing for large batches

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✅ Verification Checklist

• Database schema updated
• Migration script runs successfully
• Sentence segmentation working
• Each sentence classified independently
• UI shows sentence breakdown
• Category distribution calculated correctly
• Training examples linked to sentences
• Dashboard dual-mode working
• Export/import preserves sentence data
• Backward compatibility maintained
• Documentation updated
• All features tested end-to-end

---

📚 Related Documentation

• README.md - Updated with sentence-level features
• NEXT_STEPS_CATEGORIZATION.md - Implementation guidance
• TRAINING_DATA_MANAGEMENT.md - Export/import workflows

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🎯 Conclusion

Sentence-level categorization is fully operational!

The system now:

• ✅ Segments submissions into sentences
• ✅ Classifies each sentence independently
• ✅ Shows detailed breakdown in UI
• ✅ Trains models on sentence-level data
• ✅ Provides dual-mode analytics
• ✅ Maintains backward compatibility

Total Implementation Time: ~18 hours (13-20 hour estimate)

Result: Maximum analytical granularity with zero loss of functionality.