基于框架语义的高考语文阅读理解答案句抽取  

Frame Semantic Based Answer Sentences Extraction for Chinese Reading Comprehension in College Entrance Examination

作　　者：李国臣[1,2] 刘姝林[1] 杨陟卓[1] 李茹[1,3] 张虎[1] 钱揖丽[1] 

机构地区：[1]山西大学计算机与信息技术学院,山西太原030006 [2]太原工业学院计算机工程系,山西太原030008 [3]山西大学计算智能与中文信息处理教育部重点实验室,山西太原030006

出　　处：《中文信息学报》2016年第6期164-172,共9页Journal of Chinese Information Processing

基　　金：国家863计划(2015AA015407);国家自然科学基金(61373082;61502287;61673248);山西省科技基础条件平台建设项目(2014091004-0103);山西省回国留学人员科研资助项目(2013-015);中国民航大学信息安全测评中心开放课题基金(CAAC-ISECCA-201402);山西省高校科技创新项目(201505)

摘　　要：高考语文阅读理解问答相对普通阅读理解问答难度更大,问句抽象表述的理解需要更深层的语言分析技术,答案候选句抽取更注重与问句的关联分析,答案候选句排序更注重答案句之间的语义相关性。为此,该文提出借助框架语义匹配和框架语义关系抽取答案候选句,在排序时引入流形排序模型,通过答案句之间的框架语义相关度将排序分数进行传播,最终选取分数较高的Top-4作为答案句。在北京近12年高考语文阅读理解问答题上的准确率与召回率分别达到了53.65%与79.06%。Reading comprehension QA for Chinese College Entrance Examination is much more difficult than general reading comprehension QA in that it requires deeper linguistic analysis technology to understand the question,and the semantic correlation between the answers and questions.This paper proposes to extract the candidate answer sentences by frame semantic match and frame-frame semantic relation,and the manifold-ranking model are applied to propogate the frame semantic relevancy to decide the top-four candidate answers.The accuracy and recall on the college entrance examination of Beijing in recent twelve years is 53.65% and 79.06%,respectively.

关 键 词：高考语文 阅读理解 框架语义 答案句抽取 流形排序 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]