Modeling Factual Claims with Semantic Frames
Modeling Factual Claims with Semantic Frames
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发表时间：
2020-05
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通讯作者：
Fatma Arslan;Josue Caraballo;Damian Jimenez;Chengkai Li
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作者：
Fatma Arslan;Josue Caraballo;Damian Jimenez;Chengkai Li
在本文中，我们介绍了一个扩展的伯克利框架网的结构化和语义建模的事实索赔。建模是一种强大的工具，可以在许多不同的任务中使用，例如将索赔与现有的事实检查相匹配，并将索赔转换为结构化查询。我们的工作引入了11个新的手工制作的框架沿着9个现有的框架，所有这些框架都是在考虑到事实检查的情况下选择的。沿着这些框架，我们还提供了2,540个完全注释的句子，这些句子可以用来理解这些框架是如何工作的，并训练机器学习模型。最后，我们还发布了我们的注释工具，以方便其他研究人员对FrameNet进行自己的本地扩展。
In this paper, we introduce an extension of the Berkeley FrameNet for the structured and semantic modeling of factual claims. Modeling is a robust tool that can be leveraged in many different tasks such as matching claims to existing fact-checks and translating claims to structured queries. Our work introduces 11 new manually crafted frames along with 9 existing FrameNet frames, all of which have been selected with fact-checking in mind. Along with these frames, we are also providing 2,540 fully annotated sentences, which can be used to understand how these frames are intended to work and to train machine learning models. Finally, we are also releasing our annotation tool to facilitate other researchers to make their own local extensions to FrameNet.