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A crowdsourced system for user studies in information extraction

机译:用于信息提取中的用户研究的众包系统

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In this paper, from an entity linking (EL) system, we take a set of tweets, where some subsequence of words is annotated with possible meaning/entities and these entities are linked with several Wikipedia pages. We propose a model using crowdsourcing to disambiguate and decide about the accurate Wikipedia page that must be linked with a definite word/spot. We discuss about importance of crowdsourcing and compare different crowdsourcing systems and at the end, introduce crowdflower. We discuss about the crowdflower features in particular. Finally, we analyse output reports of the crowdflower and present a novel approach to select the reliable results. In summary, our observations show that reliable results have a confidence rate over 0.5.
机译:在本文中,我们从实体链接(EL)系统中获取了一组推文,其中在单词的子序列中标注了可能的含义/实体,并且这些实体与多个Wikipedia页面链接。我们提出了一个使用众包消除歧义的模型,并确定必须与确定的单词/地点链接的准确的Wikipedia页面。我们讨论众包的重要性,并比较不同的众包系统,最后介绍众花。我们特别讨论众花特征。最后,我们分析了众花的输出报告,并提出了一种选择可靠结果的新颖方法。总而言之,我们的观察表明,可靠的结果的置信度超过0.5。

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