Detecting Story Analogies from Annotations of Time, Action and Agency

We describe the Story Intention Graph (SIG) as a model of narrative meaning that is amenable to both corpus annotation and computational inference. The relations, focusing on time, action and agency, can express a range of thematic scenarios and lend themselves to the automatic detection of story similarity and analogy. An evaluation finds that such detection outperforms a propositional similarity metric in predicting human judgments of story similarity in the Aesop domain.

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