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Definition and Extraction of Causal Relations for QA on Fault Diagnosis of Devices

机译:QA因子关系对器件故障诊断的定义和提取

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Causal relations in ontology should be defined based on the inference types necessary to solve the tasks specific to application, as well as domain. In this paper, we present a model to define and to extract causal relations for application ontology, which is targeted, as a case study, to serve a Question-Answering (QA) system on fault-diagnosis of electronic devices. In the first phase, causal categories are defined by identifying the generic inference patterns of QA on fault-diagnosis. In the second, the semantic relations between concepts in the corpus denoting the causal categories are defined as causal relations. In the third, instances of causal relations are extracted using the lexical patterns from the definitional statements of terms in domain, and extended with information from thesaurus. On the evaluation by domain experts, our model shows precision of 92.3% in classifying relations at the definition phase and precision of 80.7% in identifying causal relations at the extraction phase.
机译:应基于解决特定于应用程序的任务所需的推断类型以及域,以及域名来定义本体中的因果关系。在本文中,我们提出了一种模型来定义和提取应用程序本体的因果关系,以案例研究为例,以服务于电子设备的故障诊断的问题答案(QA)系统。在第一阶段中,通过识别CONT故障诊断的QA通用推理模式来定义因果类别。第二,表示因果类别的语料库中概念之间的语义关系被定义为因果关系。在第三,使用来自域中的术语的定义陈述的词法模式来提取因果关系的情况,并与来自词库的信息扩展。根据领域专家的评估,我们的模型显示了定义阶段的分类关系的精度为92.3%,在识别提取阶段的因果关系时的定义相和精度为80.7%。

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