计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 154-158.doi: 10.11896/jsjkx.210100215
后同佳, 周良
HOU Tong-jia, ZHOU Liang
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。
中图分类号:
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