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Case based Indonesian closed domain question answering system with real world questions

机译:基于案例的印度尼西亚闭幕域问题,具有现实世界问题的回答系统

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Number of people having expertise in a certain domain is less than people who need information in that domain. In this situation, an automatic question answering (QA) system is necessary. Observing available manual QA sites on internet, the real world question that people usually ask have different expected answer type (EAT) compared to a common automatic QA. Addressing a case study of a religion domain which makes it a closed domain QA, we proposed the EAT into 6 types: LAW, DEFINITION, COMPARISON, METHOD, TIME and PERSON. Different with common QA approach, we built the QA system using case based approach which consists of two main components: Question Analyzer and Case Retriever. Related with the case based reasoning (CBR) framework, these two main components act as the Retrieve and Reuse process while the Revise and Retain process is handle by Case Retainer component. The QA system was built using available Indonesian Natural Language Processing (NLP) tools and FreeCBR as the CBR library. The experiments were done to calculate the accuracy and testing the system with unknown case. By using 77 cases collected from internet with assumption that all answers are available, the experiments achieved 97% accuracy. And by using 10 test cases for the unknown case, the similarity score calculated by the system showed that the test questions have no answer in the available case base.
机译:某个领域具有专业知识的人数少于需要该领域的信息的人。在这种情况下,需要一个自动问题应答(QA)系统。在互联网上观察可用的手册QA网站,与普通的自动QA相比,人们通常会提出的人通常会提出不同的预期答案类型(吃)。解决宗教领域的案例研究,使其成为封闭的域QA,我们提出吃饭6种类型:法律,定义,比较,方法,时间和人。不同于常见的QA方法,我们使用基于案例的方法构建了QA系统,由两个主要组件组成:问题分析仪和案例猎犬。与基于案例的推理(CBR)框架有关,这两个主要组件充当检索和重用过程,同时通过案例保持器组件进行修改和保留过程。 QA系统是使用可用的印度尼西亚自然语言处理(NLP)工具和Freecbr作为CBR库建造的。进行实验以计算具有未知情况的精度和测试系统。通过假设从互联网收集的77例,所有答案都可用,实验可实现97%的准确性。并且通过使用10个测试用例的未知情况,系统计算的相似度分数显示测试问题在可用案例基础上没有答案。

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