Name
Abstract | ||
|---|---|---|
While hearing a case, the judge must fully understand the case and make clear the disputed issues between parties, which is the cornerstone of a fair trial. However, manual mining the key of the case from the statements of the litigious parties is a bottleneck, which currently relies on methods like keyword searching and regular matching. To complete this time-consuming and laborious task, judges need to have sufficient prior knowledge of cases belonging to different causes of action. We try to apply the technology of event extraction to faster capture the focus of the case. However, there is no proper definition of events that contains types of focus in the judicial field. And existing event extraction methods can't solve the problem of multiple events sharing the same arguments or trigger words in a single sentence, which is very common in case materials. In this paper, we present a mechanism to define focus events, and a two-level labeling approach, which can solve multiple events sharing the same argument or trigger words, to automatically extract focus events from case materials. Experimental results demonstrate that the method can obtain the focus of case accurately. As far as we know, this is the first time that event extraction technology has been applied to the judicial field.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3341162.3345608 | Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers |
Keywords | Field | DocType |
event extraction, event-type definition, judicial field | Computer vision,Engineering drawing,Computer science,Artificial intelligence | Conference |
ISBN | Citations | PageRank |
978-4503-6869-8 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chuanyi Li | 1 | 27 | 12.92 |
| Yu Sheng | 2 | 0 | 0.34 |
| JiDong Ge | 3 | 119 | 28.39 |
| Bin Luo | 4 | 66 | 21.04 |