Judicial Intelligent Assistant System: Extracting Events from Divorce Cases to Detect Disputes for the Judge
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Abstract (arXiv)
In formal procedure of civil cases, the textual materials provided by different parties describe the development process of the cases. It is a difficult but necessary task to extract the key information for the cases from these textual materials and to clarify the dispute focus of related parties. Currently, officers read the materials manually and use methods, such as keyword searching and regular matching, to get the target information. These approaches are time-consuming and heavily depending on prior knowledge and carefulness of the officers. To assist the officers to enhance working efficiency and accuracy, we propose an approach to detect disputes from divorce cases based on a two-round-labeling event extracting technique in this paper. We implement the Judicial Intelligent Assistant (JIA) system according to the proposed approach to 1) automatically extract focus events from divorce case materials, 2) align events by identifying co-reference among them, and 3) detect conflicts among events brought by the plaintiff and the defendant. With the JIA system, it is convenient for judges to determine the disputed issues. Experimental results demonstrate that the proposed approach and system can obtain the focus of cases and detect conflicts more effectively and efficiently comparing with existing method.
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References · 10
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- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
- 2018Jointly Multiple Events Extraction via Attention-based Graph Information Aggregationcited by 73
- 2017Zero-Shot Transfer Learning for Event Extractioncited by 66
- 2015Combining Language and Vision with a Multimodal Skip-gram Modelcited by 28
- 2020Unsupervised Label-aware Event Trigger and Argument Classificationcited by 27
- 2016Bidirectional Recurrent Neural Networks for Medical Event Detection in Electronic Health Recordscited by 20
- 2020Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependenciescited by 17
- 2017Scale Up Event Extraction Learning via Automatic Training Data Generationcited by 11
- 2021COfEE: A Comprehensive Ontology for Event Extraction from textcited by 2
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