Named Entity Recognition Approach of Judicial Documents Based on Transformer
Named entity recognition is one of the key tasks in the field of natural language processing,and it is the foundation of downstream tasks.At present,there are relatively few research results on the judicial field,and there are still many problems need to be solved in the informatization and intelligent transformation of the judicial system.Compared with texts in other fields,judi-cial documents have limitations such as strong professionalism and few corpus resources,leading to low recognition results of ex-isting judicial documents.Therefore,the research is carried out from the following three aspects.Firstly,a multi-label hierarchical iterative annotation method(ML-HIA)is proposed,which can automatically annotate the original judicial documents and effec-tively improve the effect of the entity recognition task of judicial documents.Secondly,an feature mixed Transformer(FM-Trans-former)neural network model,which makes full use of the deep features of the inherent attributes of Chinese characters,is pro-posed to identify named entities of judicial documents.Finally,the proposed method and model are compared with other neural network models.The proposed method of text annotation can realize the task of judicial document annotation accurately.At the same time,compared with other models,the proposed model has a great improvement in the general dataset,and has achieved good results in the judicial datasets.
Natural language processingData annotationTransformer modelDeep learningJudicial informatization
NETL
NSTL
万方数据
