Journalists publish statements provided by people, or \textit{sources} to contextualize current events, help voters make informed decisions, and hold powerful individuals accountable. In this work, we construct an ontological labeling system for sources based on each source's \textit{affiliation} and \textit{role}. We build a probabilistic model to infer these attributes for named sources and to describe news articles as mixtures of these sources. Our model outperforms existing mixture modeling and co-clustering approaches and correctly infers source-type in 80\% of expert-evaluated trials. Such work can facilitate research in downstream tasks like opinion and argumentation mining, representing a first step towards machine-in-the-loop \textit{computational journalism} systems.

翻译：记者发表由人们或\ textit{ sources} 提供的声明, 以将当前事件背景化, 帮助选民做出知情决定, 并追究有权势的个人的责任。 在这项工作中, 我们根据每个来源的\ textit{ affiliation} 和\ textit{pole} 建立一个源的本体标签系统。 我们建立了一个概率模型, 用来推断指定来源的这些属性, 并将新闻文章描述为这些来源的混合物。 我们的模式优于现有的混合模型和联合组合方法, 并且正确推导了经专家评价的试验的80 { } 的来源类型 。 这样的工作可以促进下游任务的研究, 如意见和争论采矿, 代表着向机器运行中 流动 \ textit{computational irporty} 系统迈出的第一步 。