基于SVM-BiLSTM-CRF模型的财产纠纷命名实体识别方法

Named Entity Recognition Method of Judgment Documents with SVM-BiLSTM-CRF

裁判文书中的命名实体识别是自动化审判的关键一步,如何能够有效的分辨出案件的关键命名实体是本文的研究重点.因此本文针对财产纠纷审判案件,提出了一种基于SVM-BiLSTM-CRF的神经网络模型.首先利用SVM筛选出包含关键命名实体的句子,然后将正确包含此类实体的句子转化为字符级向量作为输入,构建适合财产纠纷裁判文书命名实体识别的BiLSTM-CRF深层神经网络模型.通过构建训练数据进行验证和对比,该模型比其他相关模型表现出更高的召回率和准确率.

The recognition of the named entity in the judgment documents is the key step in the automatic trial.How to effectively distinguish the key named entity of the case is the key point in this study.Therefore,this study proposes a neural network model based on SVM-BiLSTM-CRF for property dispute of trial cases.First,the sentences containing the key named entities are selected by SVM,and then the sentences are converted into the character level vectors as input,and the BiLSTM-CRF deep neural network model suitable for the identification of the property dispute referee’s named entity is constructed.By constructing training data for verification and comparison,the model shows higher recall and accuracy than other related models.