基于BERT-BiLSTM-CRF的法律案件实体智能识别方法

在智能法务系统应用中，人工智能自然语言处理相关技术常采用静态特征向量模型，算法效率低，精度偏差较大.为了对法律文本中的案件实体进行智能识别，提高案件的处理效率，针对动态字向量模型提出以基于转换器的双向编码表征模型作为输入层的识别方法.在其基础上通过融合双向长短期记忆网络和条件随机场模型，构建了高精度的法律案件实体智能识别方法，并通过实验验证了模型的性能.

Intelligent Identification Method of Legal Case Entity Based on BERT-BiLSTM-CRF

In the past, artificial intelligence natural language processing related technologies often used static feature vector models in the application of intelligent legal systems, which had problems such as low algorithm efficiency and large accuracy deviations. To intelligently identify case entities in legal texts and improve case processing efficiency, the dynamic word vector model is studied, and a recognition method based on the bidirectional encoder representations from transformers model as the input layer is proposed. Based on the fusion of bi-directional long short-term memory and conditional random fields models, a high-precision method of intelligent identification of legal case entities is constructed. The performance of the model is verifiedthrough experiments.