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

"Don't quote me on that": Finding Mixtures of Sources in News Articles