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:2019,28(9):203-208
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基于循环神经网络的语义完整性分析
(湘潭大学 信息工程学院, 湘潭 411105)
Semantic Integrity Analysis Based on Recurrent Neural Network
(College of Information Engineering, Xiangtan University, Xiangtan 411105, China)
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投稿时间:2019-03-11    修订日期:2019-04-04
中文摘要: 本文提出了一种基于循环神经网络的语义完整性分析方法,通过判断句子是否语义完整,将长文本切分成多个语义完整句.首先,对文本进行分词,映射为相应的词向量并进行标注,然后将词向量和标注信息通过循环窗口和欠采样方法处理后,作为循环神经网络的输入,经过训练最后得到模型.实验结果表明,该方法可以达到91.61%的准确率,为主观题自动评分工作提供了基础,同时对语义分析、问答系统和机器翻译等研究有一定的帮助.
Abstract:This study proposes a semantic integrity analysis method based on recurrent neural network. By judging whether the sentence is semantically complete, the long text is divided into multiple semantic complete sentences. First, dividing the sentences into words, mapped to the corresponding word vector and labeled. Then the word vector and the annotation information are processed by the loop window and the undersampling method, and used as the input of the recurrent neural network. Finally we get the model by training. The result of experiment indicates that this method can achieve an accuracy of 91.61%. This method is the basis of automatic assessment of the subjective questions, and also helps the research of semantic analysis, question and answer system and machine translation.
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基金项目:网络犯罪侦查湖南省普通高校重点实验室开放项目(2018WLFZZC003)
引用文本:
刘京麦野,刘新,郭炳元,孙道秋.基于循环神经网络的语义完整性分析.计算机系统应用,2019,28(9):203-208
LIU Jing-Mai-Ye,LIU Xin,GUO Bing-Yuan,SUN Dao-Qiu.Semantic Integrity Analysis Based on Recurrent Neural Network.COMPUTER SYSTEMS APPLICATIONS,2019,28(9):203-208

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