CaseLink

Code for CaseLink paper (SIGIR 2024).

Title: CaseLink: Inductive Graph Learning for Legal Case Retrieval

Author: Yanran Tang, Ruihong Qiu, Hongzhi Yin, Xue Li, and Zi Huang

Installation

Requirements can be seen in /requirements.txt

Dataset

Datasets can be downloaded from COLIEE2022 and COLIEE2023.

Specifically, the downloaded COLIEE2022 folders task1_train_files_2022 and task1_test_files_2022 should be put into /Graph_generation/COLIEE2022/.

The label file task1_train_labels_2022.json and task1_test_labels_2022.json shoule be put into folder /label/.

COLIEE2023 folders should be set in a similar way.

The final project files are as follows:

```
$ ./CaseGNN/
.
├── DATASET
│   └── data_load.py
├── Grpah_generation
│   ├── bm25   
│   │   ├── bm25_matrix
│   │   ├── bm25_coliee+lecard.py
│   │   └── bm25_model.py
│   ├── casegnn_embedding
│   ├── graph
│   │   ├── graph_bin_2022
│   │   └── graph_bin_2023
│   ├── COLIEE2022
│   ├── COLIEE2023
│   ├── graph_construction.py
│   └── federal_charges_coliee.txt
├── label 
│   ├── hard_neg_top50_train_2022.json
│   ├── hard_neg_top50_train_2023.json
│   ├── task1_test_labels_2022.json            
│   ├── task1_test_labels_2023.json 
│   ├── task1_train_labels_2022.json 
│   ├── task1_train_labels_2023.json 
│   ├── test_2022_candidate_with_yearfilter.json
│   └── test_2023_candidate_with_yearfilter.json     
├── CaseLink2022_run.sh
├── CaseLink2023_run.sh
├── Graph.sh
├── BM25_matrix_generation.sh
├── CaseLink_model.py
├── main.py
├── train.py
├── torch_metrics.py
├── requirements.txt
└── README.md          
```

Data Preparation

1. BM25 ranking matrix generation

• Run . ./BM25_matrix_generation.sh to generate COLIEE2022 bm25 ranking matrix files in folder ./Graph_generation/bm25/bm25_matrix/.

• The same process for COLIEE2023, please change the --data 2022 to --data 2023 in BM25_matrix_generation.sh.

• The BM25 ranking matrix can be also downloaded here.

2. CaseGNN case embedding generation

• To generate the CaseGNN case embeddings, please run the CaseGNN2022_run.sh and CaseGNN2023_run.sh in CaseGNN github

• The CaseGNN case embedding can be also downloaded here.

CaseLink Graph Construction

• CaseLink graph constrction utilises the result of bm25 ranking result and case embedding from CaseGNN, please ensure the folders of ./Graph_generation/bm25/bm25_matrix/ and ./Graph_generation/casegnn_embedding/ have been generated or downloaded.

• Run . ./Graph.sh

  • --topk_neighbor can be chosen from {3, 5, 10, 20}.
  • --charge_threshold can be chosen from {0.85, 0.9, 0.95}.

• The CaseLink graphs are saved in folder /Graph_generation/graph/

• The same process for COLIEE2023, please change the --data 2022 to --data 2023 in Graph.sh.

• The CaseLink graphs can be also downloaded here.

Model Training

1. CaseGNN Model Training

Run . ./CaseLink2022_run.sh and . ./CaseLink2023_run.sh for COLIEE2022 and COLIEE2023, respectively.

Cite

If you find this repo useful, please cite

@article{CaseLink,
  author       = {Yanran Tang and
                  Ruihong Qiu and
                  Hongzhi Yin and
                  Xue Li and
                  Zi Huang},
  title        = {CaseLink: Inductive Graph Learning for Legal Case Retrieval},
  journal      = {CoRR},
  volume       = {abs/2403.17780},
  year         = {2024}
}