Research And Application Of Legal Intelligence Based On Deep Learning

The purpose of legal intelligence is to give the machine the ability to read and understand legal texts and quantitative analysis cases,and to complete tasks with practical application requirements such as crime prediction,legal clause recommendation and sentence prediction.Legal intelligence is expected to assist judges,lawyers and other people to conduct legal trials more effectively,better help the people understand the application of relevant legal provisions in the case,improve the people's understanding of the law,and enhance the people's use of the law to safeguard their legitimate rights and interests.The awareness of legal intelligence has promoted the application of language understanding and artificial intelligence technology in the legal field to a certain extent,and promoted the development of artificial intelligence in the legal field.Based on the above application of artificial intelligence technology in the legal field,this paper selects the crime prediction and legal recommendation task in legal intelligence as the application of artificial intelligence technology in the field of legal intelligence,and proposes two network model structures.Solve the crime prediction and the law recommendation separately.The main research work is as follows:1.The crime prediction task,the crime prediction is based on the case description and facts in the criminal law document,predicting the accused's convicted crime.With the gradual improvement of the people's awareness of rights protection,the number of judicial cases has gradually increased,but the number of relevant personnel currently engaged in judicial work is limited.Such a situation will result in relatively low efficiency of judicial handling,while artificial intelligence has the ability to quickly process large amounts of textual information.Therefore,using the deep learning technology in the field of artificial intelligence to explore the method of solving the crime prediction task,an end-to-end memory network(ETEME)model based on deep learning is proposed to process the case description and factual information,and to solve the crime prediction task.The end-to-end memory network proposed for the crime prediction task,the model method has a large external storage memory structure,which allows the legal text to be stored in the memory structure to enhance the semantic continuous representation of the case description input,the structure and memory The network structure is the same,the memory network structure is trained in an endto-end manner,and at the same time,compared with other models during training,the end-to-end memory network model requires less supervisory information advantage.Through the comparison between the results of the experiment and the text classification model proposed by the related questions,the end-to-end memory network model experiment results are the best.Using the multi-class evaluation index F1 evaluation value,the F1 value reaches 85.1% effect.The best model before only reached 83.4%.2.The law recommends the task.The law recommendation is based on the case description and facts in the criminal law document,and predicts the relevant laws and regulations involved in the case.In the current judicial process,it is mainly to use the relevant judicial personnel to manually memorize the laws and regulations,and then analyze the facts of the case to obtain the applicable relevant laws and regulations.Due to the large number of laws and regulations,this may lead to the inaccuracy of the applicable law in the judicial process,while the deep learning technology in artificial intelligence has a strong learning ability,which can help related by using artificial intelligence technology.Judicial personnel accurately carry out the recommendation work of the applicable law.Combining the relationship between the case description and the facts and the applicable legal provisions,the author proposes a fusion of CNN-GRU network model structure to solve the rule recommendation task.Because of the convolutional neural network model in the field of text classification,the text features are extracted to represent the case description and The facts part has a good performance,but there is a rich semantic relationship between the case description and the factual part of the text context,and the convolutional neural network acquires the characteristics of the relationship between the text information before and after the case text is extracted.The representation ability is weak,and the neural network model GRU network model has a good effect in learning the long-distance dependent sequence information.Therefore,this paper proposes the GRU network model to extract the semantic relationship between the case description and the fact part,GRU network model and The convolutional neural network is fused with a new network structure model CNN-GRU.Through the experimental fusion of CNNGRU network model and other related model results,the proposed fusion CNN-GRU network structure model has achieved the best results..The accuracy rate is 98.2%,the micro-average(F-micro)is 83.2%,and the macro-average(F-macro)is 72.6%.Compared with other solutions for solving the rule-recommended task model,the fusion CNN-GRU network model has obvious improve.The main research content of this paper is to explore the crime prediction and legal recommendation task in the field of legal intelligence,and propose an end-to-end memory network model to solve the crime prediction task and integrate the CNN-GRU network model to solve the law recommendation task.Comparing and analyzing other models dealing with the same task problem,both models have achieved good results,and it is expected to be a method of assisting sentencing to a certain extent.