基于CFN和篇章主题的概括型问答题的解答  

Question Answering for Overview Questions Based on CFN and Discourse Topic

作　　者：杨陟卓[1] 李春转 张虎[1] 钱揖丽[1] 李茹[1,2] YANG Zhizhuo;LI Chunzhuan;ZHANG Hu;QIAN Yili;LI Ru(School of Computer and Information Technology,Shanxi University,Taiyuan,Shanxi 030006,China;Key Laboratory of Computation Intelligence and Chinese Information Processing of Ministry of Education,Shanxi University,Taiyuan,Shanxi 030006,China)

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]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》2020年第12期73-81,共9页Journal of Chinese Information Processing

基　　金：国家重点研发计划(2018YFB1005103);国家自然科学基金(61772324)。

摘　　要：相对于普通阅读理解,高考语文阅读理解难度更大,问句更加抽象,答案候选句的抽取除了注重与问句的相似性分析,还注重对材料内容以及作者的观点的概括归纳。因此该文提出了利用汉语框架网(Chinese FrameNet)抽取与问句语义相似的候选句的方法,通过识别篇章主题(段落主题句和作者观点句),生成与问句相关的内容要点以及作者的观点态度,最终选取top 6作为答案句。在近12年北京市高考真题上进行测试,召回率达到了68.69%,验证了该方法的有效性。Reading comprehension QA for College Entrance Examination on Chinese is much challenging due to the fact that the questions are more abstract. In addition to the question similarity analysis, the extraction of answer candidate sentences should also pay more attention to the topic and opinion sentences. This paper proposes to extract the candidate answer sentences by frame semantic match and frame semantic relation. By identifying the discourse topic sentences, the topic and opinion sentences related to the questions are generated. Then the top-six candidate answers are selected based on ranking results. In the experiment, the recall of the method on the College Entrance Examination of Beijing in recent twelve years is 68.69%, which verifies the effectiveness of the method.

关 键 词：概括型问答题 框架语义 篇章主题 答案句抽取 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]