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Relation Extraction from Wikipedia Leveraging Intrinsic Patterns

机译:利用本征模式从维基百科中提取关系

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Enormous efforts of human volunteers have made Wikipedia become a treasure of textual knowledge. Relation extraction that aims at extracting structured knowledge in the unstructured texts in Wikipedia is an appealing but quite challenging problem because it's hard for machines to understand plain texts. Existing methods are not effective enough because they understand relation types in textual level without exploiting knowledge behind plain texts. In this paper, we propose a novel framework called Athena leveraging Intrinsic Patterns which are patterns that can understand relation types in semantic level to solve this problem. Extensive experiments show that Athena significantly outperforms existing methods.
机译:人类志愿者的巨大努力使维基百科成为文本知识的宝藏。关于在维基百科的非结构化文本中提取结构化知识的关系提取是一种吸引人,但非常具有挑战性的问题,因为机器很难理解纯文本。现有方法不够有效,因为他们了解文本级别的关系类型而不利用纯文本背后的知识。在本文中,我们提出了一种名为ATHENA的新颖框架,利用了内在模式,这是可以理解语义水平中的关系类型来解决这个问题的模式。广泛的实验表明,雅典娜显着优于现有方法。

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