  Characterizing relationships between people is fundamental for the
understanding of narratives. In this work, we address the problem of inferring
the polarity of relationships between people in narrative summaries. We
formulate the problem as a joint structured prediction for each narrative, and
present a model that combines evidence from linguistic and semantic features,
as well as features based on the structure of the social community in the text.
We also provide a clustering-based approach that can exploit regularities in
narrative types. e.g., learn an affinity for love-triangles in romantic
stories. On a dataset of movie summaries from Wikipedia, our structured models
provide more than a 30% error-reduction over a competitive baseline that
considers pairs of characters in isolation.
