Intelligent BERT-BiLSTM-CRF Based Legal Case Entity Recognition Method

摘要：

　　In the past decade,the main natural language processing technologies in the field of artificial intelligence are Word2Vec and ELMO traditional models in the application of intelligent legal systems.For the reason that they are basically one-way training algorithms from left to right and only one-way information is learned,so these traditional models have some disadvantages such as low efficiency and accuracy.In order to identify specific elements in the legal case intelligently,such as time,location,perpetrator,and recipient,and improve the efficiency of case processing,a new entity recognition method using the BERT(Bidirectional Encoder Representations from Transformers)model as the input layer is proposed.The BERT model is a new type of word vector model that relies on context by joint adjusting the bidirectional Transformer in all layers.Basing on BERT model,we proposed a new method comprise BERT,BiLSTM and CRF(Conditional Random Fields)to carry on the intelligent identification of legal case entities.And with abundant experiment result,the better accuracy and efficiency of our method has been proved comparing to traditional models such as Word2Vec.

关键词： Natural language processing Intelligent Legal Affairs BERT Entity Recognition

作者: Ming Dong Sun ZhiXinGuo Xiao Long Deng

作者单位: BeiJing THUNISOFT Information Technology Corporation Limited Beijing,China Cyber Security School BeijingUniversity of Post and Telecommunication Beijing,China BeijingUniversity of Post and Telecommunication Beijing,China

会议类型: 国际会议

会议名称: 2021中国图灵大会(ACM Turing Celebration conference-China 2021

会议地点: 合肥

会议语种:英文

页码: 209-215

在线出版日期: 2021-07-30（万方平台首次上网日期，不代表论文的发表时间）