Discourse is a literary form that consists of semantically-related and well-structured arguments. One of the key tasks of discourse analysis is to resolve semantic relationship between arguments. Explicit relation is easy to detect with an accuracy of nearly 90% because of its direct cues. In contrast, implicit relation is difficult to detect with only an accuracy of nearly 40% since it has no direct cues. To solve the problem, paper proposes a hypothesis that parallel arguments normally have the consistent semantic relations. Based on the hypothesis and by utilizing the characteristics that explicit relation is easy to detect, the paper implements a method of implicit relation detection which uses explicit relation to infer implicit relation among parallel arguments. We evaluate the method on the standard penn discourse Treebank (PDTB). The experimental results show an improvement of 17.26%.%篇章是论元经过语义关联和结构化组织形成的自然语言文体.篇章分析研究的核心任务之一是解释论元的语义关系,其中,显式关系因具有直观线索而易于检测,目前检测精度高达90%;相对而言,隐式关系因缺乏直观线索而难于检测,目前精度仅约40%.针对这一问题,基于一种“论元平行则关系平行”的假设,并利用显式篇章关系易于检测的特点,通过平行论元的识别与平行关系的消歧,实现了一种显式关系平行推理隐式关系的隐式篇章关系检测方法.利用标准宾州篇章关系树库(Penn discourse TreeBank,简称PDTB)对这一检测方法进行评测,结果显示,精确率提升达17.26%.
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