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REMOD: Relation Extraction for Modeling Online Discourse
arXiv - CS - Social and Information Networks Pub Date : 2021-02-22 , DOI: arxiv-2102.11105
Matthew Sumpter, Giovanni Luca Ciampaglia

The enormous amount of discourse taking place online poses challenges to the functioning of a civil and informed public sphere. Efforts to standardize online discourse data, such as ClaimReview, are making available a wealth of new data about potentially inaccurate claims, reviewed by third-party fact-checkers. These data could help shed light on the nature of online discourse, the role of political elites in amplifying it, and its implications for the integrity of the online information ecosystem. Unfortunately, the semi-structured nature of much of this data presents significant challenges when it comes to modeling and reasoning about online discourse. A key challenge is relation extraction, which is the task of determining the semantic relationships between named entities in a claim. Here we develop a novel supervised learning method for relation extraction that combines graph embedding techniques with path traversal on semantic dependency graphs. Our approach is based on the intuitive observation that knowledge of the entities along the path between the subject and object of a triple (e.g. Washington,_D.C.}, and United_States_of_America) provides useful information that can be leveraged for extracting its semantic relation (i.e. capitalOf). As an example of a potential application of this technique for modeling online discourse, we show that our method can be integrated into a pipeline to reason about potential misinformation claims.

中文翻译:

REMOD:用于建模在线话语的关系提取

在线进行的大量讨论对民用和知情的公共领域的运作构成了挑战。由第三方事实检查员审查的使标准化在线话语数据(例如ClaimReview)的努力正在提供大量有关可能不准确的索赔的新数据。这些数据可以帮助阐明在线话语的性质,政治精英在扩大在线话语中的作用及其对在线信息生态系统完整性的影响。不幸的是,当涉及在线话语的建模和推理时,许多数据的半结构化性质提出了重大挑战。关键挑战是关系提取,这是确定索赔中命名实体之间的语义关系的任务。在这里,我们开发了一种用于关系提取的新型监督学习方法,该方法结合了图嵌入技术和语义依赖图上的路径遍历。我们的方法基于直观的观察,即沿三元组的主体和对象(例如Washington,_D.C。}和United_States_of_America)之间的实体的知识提供了可用于提取其语义关系的有用信息(即capitalOf)。作为该技术在在线话语建模中潜在应用的一个例子,我们证明了我们的方法可以集成到管道中以对潜在的错误信息主张进行推理。我们的方法基于直观的观察,即关于三元组的对象和对象(例如Washington,_D.C。}和United_States_of_America)之间路径上的实体的知识提供了可用于提取其语义关系的有用信息(即capitalOf)。作为该技术在在线话语建模中潜在应用的一个例子,我们证明了我们的方法可以集成到管道中以对潜在的错误信息主张进行推理。我们的方法基于直观的观察,即沿三元组的主体和对象(例如Washington,_D.C。}和United_States_of_America)之间的实体的知识提供了可用于提取其语义关系的有用信息(即capitalOf)。作为该技术在在线话语建模中潜在应用的一个例子,我们证明了我们的方法可以集成到管道中以对潜在的错误信息主张进行推理。
更新日期:2021-02-23
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