基于双向GRU神经网络和注意力机制的中文船舶故障关系抽取方法  

Chinese Ship Fault Relation Extraction Method Based on Bidirectional GRU Neural Network and Attention Mechanism

作　　者：后同佳 周良[1] HOU Tong-jia;ZHOU Liang(School of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)

机构地区：[1]南京航空航天大学计算机科学与技术学院,南京210016

出　　处：《计算机科学》2021年第S02期154-158,共5页Computer Science

摘　　要：随着深度学习的发展,越来越多的深度学习模型被应用到了关系抽取任务中。传统的深度学习模型不能解决长距离的学习任务,且当抽取文本的噪声较大时表现更差。针对以上两个问题,提出了一种基于双向GRU(Gated Recurrent Unit)神经网络和注意力机制的深度学习模型来对中文船舶故障语料库进行关系抽取。首先,通过使用双向GRU神经网络来提取文本特征,解决了文本的长依赖问题,同时减少了模型运行的时间损耗和迭代次数;其次,通过建立句子级别的注意力机制,提高模型对有效语句的关注度,降低噪声句子给整体关系提取效果带来的负面影响;最后,在训练集上进行训练,并在真实的测试集上计算精确率、召回率、F1的值来将该模型与现有的方法对比。With the development of deep learning,more and more deep learning models are applied to relational extraction tasks.The traditional deep learning model can not solve the long-distance learning task,and the performance of the traditional deep learning model is worse when the noise of text extraction is large.To solve the above two problems,a deep learning model based on bidirectional GRU(gated recurrent unit)neural network and attention mechanism is proposed to extract the relationship between Chinese ship faults.Firstly,by using bidirectional GRU neural network to extract text features,the problem of long dependence of text is solved,and the running time loss and iteration times of the model are also reduced.Secondly,by establishing sentence level attention mechanism,the negative impact of noisy sentences on the whole relationship extraction is reduced.Finally,the model is trained on the training set,and the accuracy,recall,and F1 values are calculated on the real test set to compare the model with existing methods.

关 键 词：船舶故障 门控循环单元 注意力机制 关系抽取 深度学习 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]