论文标题
使用Bilstm-Crf认可中国司法实体
Recognizing Chinese Judicial Named Entity using BiLSTM-CRF
论文作者
论文摘要
指定的实体识别（NER）在自然语言处理系统中起着至关重要的作用。司法是司法信息检索，实体关系提取和知识图建设的基本组成部分。但是，由于中国人的特征和司法上的高准确性要求，中国的司法人员仍然更具挑战性。因此，在本文中，我们提出了一种名为Bilstm-CRF的深度学习方法，该方法由双向长期记忆（BILSTM）和条件随机场（CRF）组成。为了进一步的准确性促进，我们建议使用自适应力矩估计（ADAM）来优化模型。为了验证我们的方法，我们对判断文件进行实验，包括在监狱外的换向，假释和临时服务，这是从中国在线判决中获得的。实验结果达到了0.876的精度，召回0.856和F1得分为0.855，这表明拟议的Bilstm-CRF具有ADAM Optimizer的优越性。
Named entity recognition (NER) plays an essential role in natural language processing systems. Judicial NER is a fundamental component of judicial information retrieval, entity relation extraction, and knowledge map building. However, Chinese judicial NER remains to be more challenging due to the characteristics of Chinese and high accuracy requirements in the judicial filed. Thus, in this paper, we propose a deep learning-based method named BiLSTM-CRF which consists of bi-directional long short-term memory (BiLSTM) and conditional random fields (CRF). For further accuracy promotion, we propose to use Adaptive moment estimation (Adam) for optimization of the model. To validate our method, we perform experiments on judgment documents including commutation, parole and temporary service outside prison, which is acquired from China Judgments Online. Experimental results achieve the accuracy of 0.876, recall of 0.856 and F1 score of 0.855, which suggests the superiority of the proposed BiLSTM-CRF with Adam optimizer.
