【作者】 姚悦；

【导师】 刘亚军；

【作者基本信息】 东南大学 ， 计算机应用技术， 2006， 硕士

【摘要】 自动问答系统自二十世纪中期出现以来一直处于快速发展之中,成为计算机领域内的研究热点。问题定位是问答系统中关键技术之一,目前大多数问答系统采用为答案库建立索引的方式来实现问题定位,达到缩小查找范围的目的。然而这些方法都存在一定的问题。本文针对远程教育特定领域的自动答疑系统,提出了利用模糊自适应谐振映射神经网络(Fuzzy ARTMAP)以及支持向量机(SVM)的方法解决问题定位。重点研究和分析问题的特征表示以及问题的定位方法。论文首先介绍了自动问答系统的发展过程、一般体系结构、常用技术和定位方法的研究现状;重点研究了答案库的结构设计和预处理、问题的特征以及面向高维空间的特征变换,并结合Fuzzy ARTMAP和SVM两个分类算法分析了基于特征表示与变换的问题定位方法;最后,给出了原型的设计与实现,并对实验的性能进行了分析,验证了论文技术的可行性。更多还原

【Abstract】 QA system has been developing rapidly from the mid of 20th century, and has become the hotspot of research in computer science area. Localization is one of key technique in QA system. Most QA systems create index for answers’collection to reduce searching range and localize, however these methods have certain weaknesses. Aiming at QA system in remote education background, this paper proposes a new method using Fuzzy ARTMAP and SVM to solve localization. This paper’s background is based on a certain subject QA system, and focuses on question feature representation and question’s localization.This paper firstly introduces the development, architecture and techniques of QA system and the status of localization, secondly focuses on the pretreatment of answers’collection, questions’feature representation and feature transformation to high dimension space, thirdly, analyses the localization method of questions with SVM and Fuzzy ARTMAP; at last, designs and implements an prototype and analyses the performance to verify the feasibility of arithmetic proposed by this paper .更多还原