Application Of BiGRU Model Based On Attention In Judicial Trials

With the development of information technology,the research and application of AI has begun to focus on the sphere of judicature.The development of AI needs a lot of data as support.A large amount of data in legal documents can meet this requirement.The intellectualization of the judicial field is the trend of the times.Automatic judgment of cases can save a lot of resources for relevant personnel,it also can bring a lot of convenience to life.Therefore,the research in this field is necessary.Through deep learning method,this article realized the task of crime prediction and article recommendation.The experiment consists of data pre-processing、experimental model design、index evaluation and result analysis composed of four sections.In this paper,the word vector representation of BERT and word2vec are used for comparative experiments;the word vector of BERT is a two-way language model,each one-way model can not only notice all the words in front of it,but also touch the words behind it.Therefore,this article propose a BERT-based model,in which the best word vector is more dependent on context information than word2vec word vector,which makes the fact description semantics more accurate.Compared with LSTM,GRU reduces a gate function,which makes it less time-consuming in training,and relative to RNN,there is no gradient disappearance problem.Bi GRU is a bidirectional GRU,which can more comprehensively represent context information.The addition of attention mechanism makes the model not only consider context information,but also select information,highlight the effective information in the text,and ignore its useless information.The experiment was designed on CAIL2018-small data set and compared with baseline method the₉4)9))value achieved 85.05%and 80.14%scores on two tasks respectively.