Legal Judgment Prediction Based on Multiclass Information Fusion
| Autor: | Rundong Guo, Yujun Li, Kongfan Zhu, Zeqiang Li, Weifeng Hu |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Multidisciplinary
Information retrieval General Computer Science Article Subject Computer science ComputingMilieux_LEGALASPECTSOFCOMPUTING QA75.5-76.95 02 engineering and technology Legal judgment Information fusion Electronic computers. Computer science 020204 information systems Civil law (legal system) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing |
| Zdroj: | Complexity, Vol 2020 (2020) |
| ISSN: | 1076-2787 |
| DOI: | 10.1155/2020/3089189 |
| Popis: | Legal judgment prediction (LJP), as an effective and critical application in legal assistant systems, aims to determine the judgment results according to the information based on the fact determination. In real-world scenarios, to deal with the criminal cases, judges not only take advantage of the fact description, but also consider the external information, such as the basic information of defendant and the court view. However, most existing works take the fact description as the sole input for LJP and ignore the external information. We propose a Transformer-Hierarchical-Attention-Multi-Extra (THME) Network to make full use of the information based on the fact determination. We conduct experiments on a real-world large-scale dataset of criminal cases in the civil law system. Experimental results show that our method outperforms state-of-the-art LJP methods on all judgment prediction tasks. |
| Databáze: | OpenAIRE |
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