Deriving Generalized Knowledge from Corpora Using WordNet Abstraction
Deriving Generalized Knowledge from Corpora Using WordNet Abstraction
复制标题
使用 WordNet 抽象从语料库中导出广义知识
DOI：
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发表时间：
2009
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通讯作者：
Lenhart K. Schubert
中科院分区：
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作者：
Benjamin Van Durme;Phillip Michalak;Lenhart K. Schubert
从文本中提取常识知识的现有工作主要局限于事实陈述，这些事实陈述了世界上可能获得的东西。我们提出了一种通过抽象大量事实来得出更强大、更普遍的主张的方法。我们的目标是将给定谓词参数的观察到的名词合并为几种主要类型，作为 WordNet 同义词集获得。结果可以被解释为限制谓词的参数位置的语义类型的一般量化句子。
Existing work in the extraction of commonsense knowledge from text has been primarily restricted to factoids that serve as statements about what may possibly obtain in the world. We present an approach to deriving stronger, more general claims by abstracting over large sets of factoids. Our goal is to coalesce the observed nominals for a given predicate argument into a few predominant types, obtained as WordNet synsets. The results can be construed as generically quantified sentences restricting the semantic type of an argument position of a predicate.