# Sentence Segmentation

Test set for evaluating and improving Vietnamese sentence boundary detection (`sent_tokenize`) in [underthesea](https://github.com/undertheseanlp/underthesea).

## Problem

The current `PunktSentenceTokenizer` in underthesea fails on several Vietnamese-specific patterns, primarily in legal text where **article titles are merged with sentence bodies** without punctuation boundaries.

### Current Results

| Category | Total | Correct | Accuracy |
|---|---:|---:|---:|
| title_content_merge | 38 | 0 | 0.0% |
| repeated_title | 13 | 0 | 0.0% |
| ellipsis | 1 | 0 | 0.0% |
| numeric_period | 1 | 0 | 0.0% |
| quoted_speech | 1 | 0 | 0.0% |
| abbreviation | 3 | 2 | 66.7% |
| article_header | 20 | 20 | 100.0% |
| article_reference | 1 | 1 | 100.0% |
| empty_input | 1 | 1 | 100.0% |
| multi_sentence | 20 | 20 | 100.0% |
| no_punctuation | 1 | 1 | 100.0% |
| single_sentence | 30 | 30 | 100.0% |
| **TOTAL** | **130** | **75** | **57.7%** |

### Key Issues

1. **Title-content merge** (38 cases, 0% accuracy): Legal article titles like "Tội trốn thuế" are followed by sentence body "Người nào thực hiện..." without punctuation. `sent_tokenize` fails to detect this boundary.

2. **Repeated title** (13 cases, 0% accuracy): Pattern "X X là..." where the title is repeated as the subject of a definition. E.g., "Hợp đồng mượn tài sản Hợp đồng mượn tài sản là..."

3. **Ellipsis handling** (0% accuracy): "..." mid-sentence causes incorrect split.

4. **Numeric periods** (0% accuracy): Periods in numbers like "1.500.000" can cause false boundaries.

### Trained Punkt Model Results

Trained NLTK PunktTrainer on Vietnamese text from 4 sources (Wikipedia, news, books, legal documents). The trained model fixes punctuation-related issues but cannot address structural patterns (title-content merge).

| Category | Total | Baseline | Trained | Change |
|---|---:|---:|---:|---|
| title_content_merge | 38 | 0 | 0 | — |
| repeated_title | 13 | 0 | 0 | — |
| ellipsis | 1 | 0 | 1 | +1 |
| numeric_period | 1 | 0 | 1 | +1 |
| quoted_speech | 1 | 0 | 0 | — |
| abbreviation | 3 | 2 | 2 | — |
| article_header | 20 | 20 | 20 | — |
| article_reference | 1 | 1 | 1 | — |
| empty_input | 1 | 1 | 1 | — |
| multi_sentence | 20 | 20 | 18 | -2 |
| no_punctuation | 1 | 1 | 1 | — |
| single_sentence | 30 | 30 | 30 | — |
| **TOTAL** | **130** | **75** | **75** | **0** |

**Improvements**: ellipsis handling (+1), numeric period handling (+1)
**Regressions**: multi_sentence (-2) — trade-off from better ellipsis handling (`...` followed by new sentence) and quote tokenization differences.

**Conclusion**: Punkt (trained or not) cannot solve title-content merge (51/130 failures = 39% of test set) because these require structural understanding beyond punctuation disambiguation. A different approach is needed for this category.

## Files

- `test_cases.json` — 130 test cases with input, expected output, category, and domain
- `evaluate.py` — Evaluation script (`--improved` flag for trained model)
- `eval_results.json` — Detailed evaluation results
- `train_punkt.py` — Fetch data + train Punkt model
- `punkt_params_trained.json` — Trained model parameters (672 abbreviations, 378 sentence starters, 3264 collocations)
- `sent_tokenize.py` — Tokenizer using trained model

## Test Case Format

```json
{
  "id": "vlc-6200",
  "input": "Tội ngược đãi tù binh , hàng binh Người nào ngược đãi tù binh ...",
  "expected": [
    "Tội ngược đãi tù binh , hàng binh",
    "Người nào ngược đãi tù binh , hàng binh , thì bị phạt ..."
  ],
  "category": "title_content_merge",
  "domain": "legal",
  "issue": "Title merged with sentence body without boundary"
}
```

## Usage

```bash
# Run baseline evaluation (underthesea)
python evaluate.py
python evaluate.py -v

# Run trained model evaluation
python evaluate.py --improved -v

# Train Punkt model from scratch
python train_punkt.py
```

## Data Source

Test cases derived from [undertheseanlp/UDD-1](https://huggingface.co/datasets/undertheseanlp/UDD-1) across 5 domains: legal (VLC), news (UVN), Wikipedia (UVW), fiction (UVB-F), non-fiction (UVB-N).

## Categories

| Category | Description | Count |
|---|---|---:|
| title_content_merge | Article title + body merged without punctuation | 38 |
| single_sentence | Normal sentence, should not be split | 30 |
| article_header | Article header like "Quyền X Y 1 ." | 20 |
| multi_sentence | Two concatenated sentences, should be split | 20 |
| repeated_title | "X X là..." definition pattern | 13 |
| abbreviation | TS., PGS., TP. should not cause splits | 3 |
| ellipsis | "..." should not split mid-sentence | 1 |
| numeric_period | Periods in numbers should not split | 1 |
| article_reference | "Điều N ." boundary | 1 |
| quoted_speech | Periods inside quotes | 1 |
| empty_input | Empty string input | 1 |
| no_punctuation | Text without punctuation | 1 |