Named entity recognition of legal documents based on cascade model

Aiming at the problems of long legal document entities and the lack of annotated data, a cascade model that integrates the characteristics of characters and words is proposed. The traditional NER is decomposed into two cascaded subtasks: the entity recognition and the attribute recognition. The model obtains the vector representation of text character-level and word-level through BERT and Self-attention, respectively. Then the BiLSTM is used to obtain the internal features of the serialized text. Subsequenly, CRF is used to select the entity's optimal tag sequence and the attributed optimal tag sequence. Finally, these two sequences are spliced to obtain the optimal marker sequence. In order to improve the utilization rate of the data, the label linearization is introduced for the data enchancement. The results show that the method is superior to traditional models, can effectively extract named entities of legal documents, and has the vital practical significance.