Legal Event Detection Model Based on ERNIE 3.0-CRF and Ensemble Learning

Recognizing events is a prerequisite for making judgments, so discovering events in legal documents is important for the analysis of legal case. Legal Event Detection aims to extract legal event trigger words from legal documents and categorize events and is the basis of many judicial AI applications. Oriented to the needs of understanding and analyzing legal events in the judicial trial process, and in response to the low accuracy of event detection caused by the many types of events and little sample data in the legal domain, this paper constructs a pre-training model oriented to event detection in the legal domain, which makes full use of ERNIE 3.0's powerful semantic representation learning capability and CRF's effective modeling capability for sequence annotation tasks. In addition, we further enhance the effectiveness of the model through data augmentation and ensemble learning. The experimental results show that the method works better compared to the baseline model and improves the prediction accuracy of legal event detection.