Name
Abstract | ||
|---|---|---|
AbstractLegal judgment prediction, which aims at predicting judgment results such as penalty, charges, and statutes for cases, has attracted much attention recently. In this article, we focus on building a recommender system to predict the associated statutes for a case given the facts of the case as input. For this purpose, we propose a two-step neural network-based machine learning framework to assist judges as well as ordinary people to reduce their effort in finding applicable statutes. The proposed model takes advantage of recurrent neural networks with a max-pooling layer to obtain contextual representations of documents, i.e., the facts associated with the cases. Moreover, an attention mechanism is used to automatically focus on the important words contributing to the prediction of statutes. In addition, we apply an encoder--decoder ranking approach to extract correlations between statutes to achieve more accurate recommendation results. We evaluate our model on a real-world dataset. Experimental results show that, compared with existing baseline methods, our method can predict statutes that are more likely to appear in real judgments. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3424671 | ACM Transactions on Knowledge Discovery from Data |
Keywords | DocType | Volume |
Statute recommendation, recommender system, neural networks, learn to rank, multi-label classification, semantic representation | Journal | 15 |
Issue | ISSN | Citations |
2 | 1556-4681 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Feng | 1 | 3 | 4.15 |
| Chuanyi Li | 2 | 27 | 12.92 |
| JiDong Ge | 3 | 119 | 28.39 |
| Bin Luo | 4 | 66 | 21.04 |
| Vincent Ng | 5 | 1396 | 96.63 |