CAJ | 학술논문

针对中文问题分类方法中布尔模型提取特征信息损失较大的问题,提出了一种新的特征权重计算方法.在提取问题特征时,通过把信息熵算法和医院本体概念模型结合在一起,进行问题的特征模型计算,在此基础上使用支持向量机方法进行中文问题分类.在城域医院问答系统的中文问题集上进行实验,证明了该方法的有效性,大类准确率及小类准确率分别达到89.0%和87.1%,取得了较好的效果.
침대중문문제분류방법중포이모형제취특정신식손실교대적문제,제출료일충신적특정권중계산방법.재제취문제특정시,통과파신식적산법화의원본체개념모형결합재일기,진행문제적특정모형계산,재차기출상사용지지향량궤방법진행중문문제분류.재성역의원문답계통적중문문제집상진행실험,증명료해방법적유효성,대류준학솔급소류준학솔분별체도89.0%화87.1%,취득료교호적효과.
Aimed at the problem of greater information loss to use Boolean model to extract the feature during Chinese question classification, a new method which calculated feature weight is proposed. When the question feature is extracted, the model of question feature weight is calculated by a combination of information entropy algorithm and hospital ontology concept model. On that basis, the method of Support Vector Machine is used to classify Chinese questions. The classification method is tested on Chinese question set of the city-domain hospital question answering system. This method is proved to be effective and a bet-ter result is achieved. Results show that the accuracy of coarse class and fine class achieves 89.0%and 87.1%.