Datasets:

overthelex
/

ua-statute-retrieval

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 (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

Co-citation predictability decays 33--47% over 12 years (Mann-Kendall p < 0.005)
Decay is non-uniform: criminal procedure maintains stable co-citation patterns (MRR ~0.40); civil law degrades from 0.35 to 0.15
Neither BM25 nor dense retrieval (E5-large, BGE-M3) escapes temporal degradation
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}:

Mask each article ai in turn
Score all candidate articles using remaining citations as seed
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

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

@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.

Related Datasets

overthelex/ua-court-citation-graph -- Co-citation graph (99.5M decisions)
overthelex/ukrainian-court-decisions -- Court decision metadata