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Contextual Commonsense Knowledge Acquisition from Social Content by Crowd-Sourcing Explanations

机译:通过人群采购解释从社会内容中获取的上下文型号知识

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Contextual knowledge is essential in answering questions given specific observations. While recent approaches to building commonsense knowledge bases via text mining and/or crowdsourcing are successful, contextual knowledge is largely missing. To address this gap, this paper presents SocialExplain, a novel approach to acquiring contextual commonsense knowledge from explanations of social content. The acquisition process is broken into two cognitively simple tasks: to identify contextual clues from the given social content, and to explain the content with the clues. An experiment was conducted to show that multiple pieces of contextual commonsense knowledge can be identified from a small number of tweets. Online users verified that 92.45% of the acquired sentences are good, and 95.92% are new sentences compared with existing crowd-sourced commonsense knowledge bases.
机译:背景知识对于在给出特定观察的回答问题方面至关重要。虽然最近通过文本挖掘和/或众包建立了勤义知识库的方法是成功的,但上下文知识在很大程度上缺失。为了解决这一差距,本文提出了社会兴奋剂,这是一种从社会内容解释获取中文型号知识的新方法。收购过程被分成了两个认知简单的任务:识别来自给定社交内容的上下文线索,并用线索解释内容。进行了一个实验,表明可以从少数推文中识别多个上下文致辞知识。在线用户核实,92.45%的获得句子良好,95.92%是新句子,与现有的人群沟通知识库相比。

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