基于神经网络的篇章标题选择求解  

Solution of Text Title Selection Based on Neural Network

作　　者：吕国英[1] 关勇 李茹[1,2,3] 郭少茹 谭红叶 Lv Guoying;GUAN Yong;LI Ru;GUO Shaoru;TAN Hongye(School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China;Collaborative Innovation Center of Big Data Mining and Intelligent Technology in Shanxi,Taiyuan 030006,China;Key Laboratory of Ministry of Education for Computation Intelligence and Chinese Information Processing,Shanxi University,Taiyuan 030006,China)

机构地区：[1]山西大学计算机与信息技术学院,太原030006 [2]山西大学计算智能与中文信息处理教育部重点实验室,太原030006 [3]大数据挖掘与智能技术山西省协同创新中心,太原030006

出　　处：《计算机工程》2018年第9期171-176,共6页Computer Engineering

基　　金：国家高技术研究发展计划项目(2015AA015407);国家自然科学基金(61373082);山西省回国留学人员科研项目(2013-015);山西省科技基础条件平台建设项目(2014091004-0103);中国民航大学信息安全测评中心开放课题基金(CAAC-ISECCA-201402)

摘　　要：为了从候选项中选择一个最合适的答案作为文章的标题,构建一个融合卷积神经网络和循环神经网络的模型。该模型包含卷积神经网络可用于提取数据局部特征的特点,结合循环神经网络捕捉句子上下文之间关联信息的特性,通过挖掘文章与标题之间的相关性,实现标题选择的功能。测试结果表明,该模型能够有效缩短文本篇幅,提高提取的准确性。To choose the most appropriate answer from the waiting list as the title of the article,this paper proposes a model which is based on Convolutional Neural Network(CNN)and Recurrent Neural Network(RNN).The model contains the characteristics of extracting local data features from convolutional neural network,combines the characteristics of capturing association information between sentence contexts from recurrent neural network.The method excavates correlation from the article and the title to select the best answer.Experimental results show that this model can effectively shorten the length of text and improve the accuracy of extraction.

关 键 词：阅读理解 标题选择 神经网络 相关性 循环神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]