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 se- lect 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 ques- tions of recent 10 years verifies the validity of the method.
作者
关勇
吕国英
李茹
郭少茹
谭红叶
GUAN Yong;LV Guoying;LI Ru;GUO Shaoru;TAN Hongyel(School of Computer and Information Technology,Shanxi University,Taiyuan,Shanxi 030006,China;Key Laboratory of Ministry of Education for Computation Intelligence and Chinese Information Processing,Shanxi University,Taiyuan,Shanxi 030006,China;Collaborative Innovation Center of Big Data Mining and Intelligent Technology in Shanxi,Taiyuan,Shanxi 030006,China)
出处
《中文信息学报》
CSCD
北大核心
2018年第6期28-35,43,共9页
Journal of Chinese Information Processing
基金
国家863计划(2015AA015407)
国家自然科学基金(61772324
61673248)
关键词
高考语文
阅读理解
标题选择
神经网络
标题结构
相关度矩阵
college entrance examination on Chinese
reading comprehension
title selection
neural network
titlestructure
correlation matrix
