We present an unsupervised model for the discovery and clustering of latent "personas" (characterizations of entities). Our model simultaneously clusters documents featuring similar collections of personas. We evaluate this model on a collection of news articles about immigration, showing that personas help predict the coarse-grained framing annotations in the Media Frames Corpus. We also introduce automated model selection as a fair and robust form of feature evaluation.
For example, REF provide an unsupervised model that clusters articles with similar collections of "personas" (i.e., characterisations of entities) and demonstrate that these personas can help predict the coarse-grained framing annotations in the MFC.
Analyzing Framing through the Casts of Characters in the News
EMNLP
Computer Science