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Open Language Learning for Information Extraction

机译:用于信息提取的开放语言学习

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摘要

Open Information Extraction (IE) systems extract relational tuples from text, without requiring a pre-specified vocabulary, by identifying relation phrases and associated arguments in arbitrary sentences. However, state-of-the-art Open IE systems such as ReVerb and WOE share two important weaknesses -they extract only relations that are mediated by verbs, and they ignore context, thus extracting tuples that are not asserted as factual. This paper presents ollie, a substantially improved Open IE system that addresses both these limitations. First, ollie achieves high yield by extracting relations mediated by nouns, adjectives, and more. Second, a context-analysis step increases precision by including contextual information from the sentence in the extractions. ollie obtains 2.7 times the area under precision-yield curve (AUC) compared to ReVerb and 1.9 times the AUC of WOE~(parse).
机译:开放信息提取(IE)系统通过识别任意句子中的关系短语和相关自变量,从文本中提取关系元组,而无需预先指定的词汇表。但是,最新的Open IE系统(例如ReVerb和WOE)具有两个重要的弱点-它们仅提取动词介导的关系,而它们忽略上下文,从而提取未断言为事实的元组。本文介绍了ollie,这是一个经过重大改进的Open IE系统,可以解决这两个局限性。首先,ollie通过提取名词,形容词等介导的关系来获得高收益。其次,上下文分析步骤通过将句子中的上下文信息包括在提取中来提高准确性。与ReVerb相比,ollie的精确产量曲线(AUC)面积为2.7倍,WOE〜(解析)的AUC的面积为1.9倍。

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