Chinese Legal Document Named Entity Recognition Based on MVBCN-FLW
Recognition of named entities in Chinese legal documents is a basic task in the judicial field.At present,some achievements have been made in the research of named entity recognition of Chinese legal documents,but most of them rely on marked legal corpus without effective use of unlabeled legal corpus,and can not deeply obtain the characteristics of legal documents.In order to solve the above problems,this paper proposes a named entity recognition framework for Chinese legal documents.Firstly,the framework uses the converter model based on bidirectional encoder to learn the vec-tor representation of Chinese legal documents,and uses the bidirectional long-term and short-term memory network lan-guage model which can integrate the characteristics of legal terms to capture the context feature vectors of legal document sequences.Secondly,the framework fuses the vector representation of Chinese legal documents with the context feature vector,and the fused feature vector is input into a structure composed of two-way gated cycle unit,self-attention mecha-nism and conditional random field for training.In addition,in order to make the framework more fully trained when it lacks the legal corpus that has been marked,this paper uses the unlabeled legal corpus for self-training,generates the newly marked legal corpus and merges it with the initially marked legal corpus,and improves the framework performance through iterative training.Experimental results show that this framework is superior to other named entity recognition models based on mainstream neural networks.
万方数据
维普
