README.md

---
language:
- uk
license: cc-by-nc-sa-4.0
size_categories:
- 1K<n<10K
task_categories:
- text-retrieval
pretty_name: UA-StatuteRetrieval
tags:
- legal
- court-decisions
- citation-graph
- statute-retrieval
- ukrainian
- benchmark
- temporal-degradation
configs:
- config_name: ablation_comparison
  data_files:
  - split: train
    path: ablation_comparison/train-*
- config_name: article_performance
  data_files:
  - split: train
    path: article_performance/train-*
- config_name: embedding_drift
  data_files:
  - split: train
    path: embedding_drift/train-*
- config_name: sliding_window
  data_files:
  - split: train
    path: sliding_window/train-*
- config_name: temporal_metrics
  data_files:
  - split: train
    path: temporal_metrics/train-*
- config_name: temporal_reweighting
  data_files:
  - split: train
    path: temporal_reweighting/train-*
dataset_info:
- config_name: temporal_metrics
  features:
  - name: year
    dtype: int64
  - name: evaluation
    dtype: string
  - name: method
    dtype: string
  - name: mrr
    dtype: float64
  - name: hit10
    dtype: float64
  - name: n_predictions
    dtype: int64
  - name: n_cases
    dtype: int64
  splits:
  - name: train
    num_bytes: 3312
    num_examples: 45
  download_size: 4389
  dataset_size: 3312
- config_name: temporal_reweighting
  features:
  - name: eval_year
    dtype: int64
  - name: lambda
    dtype: float64
  - name: mrr_aa
    dtype: float64
  - name: hit10_aa
    dtype: float64
  - name: n_predictions
    dtype: int64
  - name: n_cases
    dtype: int64
  - name: n_articles
    dtype: int64
  splits:
  - name: train
    num_bytes: 1680
    num_examples: 30
  download_size: 4093
  dataset_size: 1680
---

# UA-StatuteRetrieval: A 20-Year Statute Retrieval Benchmark from 396M Ukrainian Court Citations

A large-scale benchmark for evaluating temporal stability of legal statute retrieval methods. Ground truth is derived exhaustively from 396 million codex-article citations extracted from 101 million Ukrainian court decisions (2007--2026).

**Paper:** [Temporal Decay of Co-Citation Predictability: A 20-Year Statute Retrieval Benchmark from 396M Ukrainian Court Citations](https://huggingface.co/papers/2605.17639) (Ovcharov, 2025)

## Dataset Configs

| Config | Description | Rows |
|--------|-------------|-----:|
| `article_performance` | Per-article MRR, Hit@5, degree for 3,667 articles (2024 snapshot) | 3,667 |
| `ablation_comparison` | Original / fixed-article / train-test MRR per year (2008--2024) | 9 |
| `sliding_window` | Mitigation experiment: MRR by eval year x window size (1, 3, 5, 10, all) | 30 |
| `embedding_drift` | Per-article semantic drift (cosine distance 2012 -> 2024) via E5-large | 116 |

## Key Findings

1. **Co-citation predictability decays 33--47% over 12 years** (Mann-Kendall p < 0.005)
2. **Decay is non-uniform**: criminal procedure maintains stable co-citation patterns (MRR ~0.40); civil law degrades from 0.35 to 0.15
3. **Neither BM25 nor dense retrieval (E5-large, BGE-M3) escapes temporal degradation**
4. **Sliding-window mitigation improves MRR by 3--28%** over cumulative indexing

## Evaluation Protocol

**Leave-one-out on the full bipartite citation graph:**

For each court decision with cited articles {a1, ..., an}:
1. Mask each article ai in turn
2. Score all candidate articles using remaining citations as seed
3. Compute rank of masked article among non-seed candidates

### Baselines (2024 snapshot)

| Method | Hit@10 | MRR |
|--------|:------:|:---:|
| Adamic-Adar | 0.545 | 0.272 |
| Common Neighbors | 0.534 | 0.266 |
| E5-large (dense) | 0.192 | 0.090 |
| BGE-M3 (dense) | 0.240 | 0.096 |
| BM25 (lexical) | 0.082 | 0.047 |
| Degree Baseline | 0.111 | 0.059 |

### Difficulty Stratification

| Bin | Articles | MRR (AA) |
|-----|:--------:|:--------:|
| Hub (>100K cites) | 21 | 0.536 |
| High (10K--100K) | 354 | 0.274 |
| Mid (1K--10K) | 864 | 0.074 |
| Low (100--1K) | 1,689 | 0.020 |
| Rare (<100) | 739 | 0.010 |

## Usage

```python
from datasets import load_dataset

# Per-article performance (2024)
articles = load_dataset("overthelex/ua-statute-retrieval", "article_performance", split="train")
print(articles.to_pandas()[["target_article", "degree", "mrr_aa"]].head(10))

# Temporal ablation (Fig 2 in paper)
ablation = load_dataset("overthelex/ua-statute-retrieval", "ablation_comparison", split="train")
print(ablation.to_pandas())

# Sliding-window mitigation (Table 6 in paper)
sw = load_dataset("overthelex/ua-statute-retrieval", "sliding_window", split="train")
print(sw.to_pandas().pivot(index="eval_year", columns="window_label", values="mrr_aa"))

# Embedding drift (Fig 9 in paper)
drift = load_dataset("overthelex/ua-statute-retrieval", "embedding_drift", split="train")
print(drift.to_pandas().groupby("law_number")["drift"].mean().sort_values(ascending=False))
```

## Citation

```bibtex
@article{ovcharov2025statute,
  title={Temporal Decay of Co-Citation Predictability: A 20-Year Statute Retrieval Benchmark from 396M Ukrainian Court Citations},
  author={Ovcharov, Volodymyr},
  year={2025}
}
```

## Source Data

Derived from the Ukrainian Unified State Register of Court Decisions (EDRSR, https://reyestr.court.gov.ua). Raw citation data (502M records) remains proprietary; the co-citation graph is available separately at [overthelex/ua-court-citation-graph](https://huggingface.co/datasets/overthelex/ua-court-citation-graph).

## Related Datasets

- [overthelex/ua-court-citation-graph](https://huggingface.co/datasets/overthelex/ua-court-citation-graph) -- Co-citation graph (99.5M decisions)
- [overthelex/ukrainian-court-decisions](https://huggingface.co/datasets/overthelex/ukrainian-court-decisions) -- Court decision metadata