Achieving the large-scale production of illegal and criminal intelligence on the dark web is a crucial preliminary task for combating illegal and criminal activities on the dark web.Current research struggles to address the issue of insufficient dark web data and primarily focuses on Western language dark web data.In order to achieve targeted analysis of Chinese dark web texts,this paper proposes a multi-task learning model for BERT-BiLSTM illegal and criminal classification and named entity recognition based on multi-task learning.It shares the BERT-BiLSTM layer between the text classification and named entity recognition tasks,and adopts the fully connected layer and the Conditional Random Field(CRF)layer as the output layers for text classification and entity recognition respectively,so as to strengthen knowledge sharing between different tasks.The experimental results on the self-constructed Chinese dark web dataset show that,compared with the baseline model,this multi-task learning model achieves certain performance improvements in both types of tasks.
维普
万方数据
