论文标题
负面陈述被认为有用
Negative Statements Considered Useful
论文作者
论文摘要
关于著名实体及其属性的知识库（KB）是搜索，问题回答和对话等应用中的重要资产。所有流行的KBS实际上仅捕获积极的陈述，并放弃对KB中未存储的陈述采取任何立场。本文证明了明确说明不持有的显着陈述的理由。负面的陈述对于克服主要针对积极问题的问题答案系统的局限性很有用；他们还可以为实体提供信息摘要。由于这种无效的陈述，任何编译它们的努力都需要通过显着性来解决排名。我们基于同伴组相关实体的正面陈述的期望，提出了一种用于编译和排名负面陈述的统计学方法。通过各种数据集的实验结果表明，该方法可以有效地发现显着的负面陈述，外部研究强调了它们对实体摘要的有用性。数据集和代码作为进一步研究的资源发布。
Knowledge bases (KBs) about notable entities and their properties are an important asset in applications such as search, question answering and dialogue. All popular KBs capture virtually only positive statements, and abstain from taking any stance on statements not stored in the KB. This paper makes the case for explicitly stating salient statements that do not hold. Negative statements are useful to overcome limitations of question answering systems that are mainly geared for positive questions; they can also contribute to informative summaries of entities. Due to the abundance of such invalid statements, any effort to compile them needs to address ranking by saliency. We present a statisticalinference method for compiling and ranking negative statements, based on expectations from positive statements of related entities in peer groups. Experimental results, with a variety of datasets, show that the method can effectively discover notable negative statements, and extrinsic studies underline their usefulness for entity summarization. Datasets and code are released as resources for further research.
