面向高考语文阅读理解的篇章标题选择研究
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摘要
高考语文阅读理解篇章标题选择题要求机器根据对篇章内容的理解,从多个候选项中选取能够准确恰当的概括表达篇章内容的选项。标题往往是高度凝练且能准确表达文意、结构鲜明的词串。因此,如何对篇章内容进行归纳概括、对标题结构进行梳理和分析是解答篇章标题选择题的关键。针对该问题,提出了标题与篇章要点相关性分析模型。该模型通过分析标题与篇章要点的相关性,构建了基于标题和篇章要点的相关度矩阵。在此基础上融入标题结构特征,选取与篇章最相关的标题。在全国近10年高考真题和测试题上进行实验,验证了该方法的有效性。
Discourse title selection for reading comprehension in the college entrance examination on Chinese is to select the best option by summarizing and analyzing the articles.The title usually captures the meaning of the article accurately in a distinctive structure.Summarizing information about the article and analyzing the title structure is the key to solve the problem.This paper proposes a correlation analysis model based on title and discourse key-points to solve the problem.This model constructs a correlation matrix of title and the discourse key-points,selecting the best answer is jointly with the title structure features.The experiment on the national college entrance examination questions of recent 10 years verifies the validity of the method.
Discourse title selection for reading comprehension in the college entrance examination on Chinese is to select the best option by summarizing and analyzing the articles.The title usually captures the meaning of the article accurately in a distinctive structure.Summarizing information about the article and analyzing the title structure is the key to solve the problem.This paper proposes a correlation analysis model based on title and discourse key-points to solve the problem.This model constructs a correlation matrix of title and the discourse key-points,selecting the best answer is jointly with the title structure features.The experiment on the national college entrance examination questions of recent 10 years verifies the validity of the method.
引文
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[2]刘知远,孙茂松,林衍凯等.知识表示学习研究进展[J].计算机研究与发展,2016,53(2):247-261.
[3]郭少茹,张虎,钱揖丽等.面向高考阅读理解的句子语义相关度[J].清华大学学报(自然科学版),2017,57(6):575-579.
[4]李茹,马淑晖,张虎等.阅读理解答案预测[J].山西大学学报(自然科学版),2017,40(4):763-770.
[5]Wang H,Bansal M,Gimpel K,et al.Machine comprehension with syntax,frames,and semantics[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing(Volume 2:Short Papers),2015,2:700-706.
[6]Narasimhan K,Barzilay R.Machine comprehension with discourse relations[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing(Volume 1:Long Papers),2015,1:1253-1262.
[7]Matthew Richardson,Christopher J C Burges,Erin Renshaw.MCTest:a challenge dataset for the opendomain machine comprehension of text[C]//Proceedings of the 2013on Empirical Methods in Natural Language Processing,2013:193-203.
[8]Wenpeng Yin,Sebastian Eberts Hinrich Schutze.Attention-based Convolutional Neural Network for Machine Comprehension[C]//Proceedings of 2016NAACL Human-Computer Question Answering Workshop,2016:15-21.
[9]Cui Y M,Liu T,Chen Z P,et al.20016a.Consensus Attention-based Neural Networks for Chinese Reading Comprehension[EB/OL].[2016].http://arxiv.org/abs/1607.02250.
[10]Cui Y,Chen Z,Wei S,et al.Attention-over-attention neural networks for reading comprehension[J].arxiv:1607.04423.2016.
[11]Kadlec R,Schmid M,Bajgar O,et al.Text understanding with the attention sum reader network[EB/OL].[2016].http://arxiv.org/abs/1603.01547.
[12]Sachan M,Dubey A,Xing E P,et al.Learning answerentailing structures for machine comprehension[C]//Proceedings of ACL,2015:239-249.
[13]Yin W P,Sebastian Ebert,Hinrich Schutze.Attention-Based Convolutional Neural Network for Machine Comprehension[EB/OL].[2016].http://arxiv.org/abs/1602.04341.
[14]Iyyer M,Boyd-Graber J L,Claudino L M B,et al.A Neural Network for Factoid Question Answering over Paragraphs[C]//Proceedings of the EMNLP,2014:633-644.
[15]Yu L,Hermann K M,Blunsom P,et al.Deep learning for answer sentence selection[EB/OL].[2014].http://arxiv.org/abs/1412.1632.
[16]刘金岭,谈芸,李健普等.基于多因素的中文文本主题自动抽取方法[J].计算机技术与发展,2010,20(7):72-79.
[17]张云涛,龚玲,王永成.基于综合方法的文本主题句的自动抽取[J].上海交通大学学报,2006,40(5):771-774.
[18]Graves A,Mohamed A,Hinton G.Speech recognition with deep recurrent neural networks[C]//Proceedings of the Acoustics,speech and signal processing(icassp),2013 IEEE international conference on.IEEE,2013:6645-6649.
[19]Tan M,dos Santos C N,Xiang B,et al.Improved Representation Learning for Question Answer Matching[C]//Proceedings of the ACL1,2016.
[20]Mikolov T,Chen K,Corrado G,Dean J.Efficient estimation of word representations in vector space[C]//Proceedings of the CoRR,abs/1301.3781,2013.
[21]Chen D,Bolton J,Christopher D.Manning.A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task[C]//Proceedings of the ACL.2016.
[22]Feng M,Xiang B,Glass M R,et al.Applying deep learning to answer selection:A study and an open task[C]//Proceedings of,2015IEEE Workshop on Automatic Speech Recognition and Understanding(ASRU).IEEE,2015:813-820.
[23]史顺良,任育新.语言学类学术文章标题的结构及其语用功能:调查与分析[J].外语教学,2010,31(4):28-28.
[24]曹杨,赵硕.科技论文标题的结构和语言特征——以和为例[J].外语教学,2014,35(2):35-39.
[25]姜亚军.复合式论文标题之争:评述与启示[J].外语教学,2010,31(6):29-32.
[26]Berant J,Srikumar V,et al.Modeling biological processes for reading comprehension[C]//Proceedings of Empirical Methods in Natural Language Processing,2014:1499-1510.
[27]王智强,李茹,梁吉业等.基于汉语篇章框架语义分析的阅读理解问答研究[J].计算机学报,2016,39(4):795-807.