Judicial Event Detection Model Based on Continuous Pre-training and Segment Pooling
The task of event detection as a Natural Language Processing(NLP)task aims to identify and classify trigger words from the text,enabling advanced text analysis and semantic understanding.Due to the scarcity of data in the judicial field and the fact that a sentence often contains multiple trigger words,our research continues to pre-train BERT with 120000 pieces of collected judicial crime data during the pre-training phase to enhance the under-standing of judicial knowledge.During the fine-tuning phase,we propose a partitioned pooling structure combined with PGD adversarial training to capture the semantic features of the trigger word context and the overall sentence.This model achieved notable performance improvement in the CAIL 2022 event detection track,with an average 3.0%improvement of F1-score than that of the BERT-based baseline model.
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