Name
Abstract | ||
|---|---|---|
In the trial process, it is difficult and tedious for judges to find appropriate statutes to decide cases, especially complicated cases. In this paper, we propose a method to recommend statutes that are applicable to judging new cases for judges. Our method utilizes the associations between causes of action and statutes as well as the co-occurrence among statutes to predict applicable statutes based on Artificial Neural Networks. The experiment data are all from the real court judgments. Our experimental results show that our method can effectively and accurately recommend statutes that are more likely to appear in real judgments. The proposed method gets better results compared to several baselines. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-97310-4_37 | PRICAI 2018: TRENDS IN ARTIFICIAL INTELLIGENCE, PT II |
Keywords | Field | DocType |
Statutes recommendation, Multi-label classification | Statute,Computer science,Co-occurrence,Multi-label classification,Artificial intelligence,Artificial neural network,Machine learning | Conference |
Volume | ISSN | Citations |
11013 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 5 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Feng | 1 | 3 | 4.15 |
| JiDong Ge | 2 | 119 | 28.39 |
| Chuanyi Li | 3 | 27 | 12.92 |
| Li Kong | 4 | 5 | 2.42 |
| Feifei Zhang | 5 | 61 | 19.93 |
| Bin Luo | 6 | 66 | 21.04 |