Inferring Interpersonal Relations in Narrative Summaries

TL;DR

This paper presents a model for inferring the polarity of interpersonal relationships in narrative summaries by combining linguistic, semantic, and social structure features, achieving significant error reduction.

Contribution

It introduces a joint structured prediction model and a clustering approach for analyzing relationships in narratives, improving over baseline methods.

Findings

Over 30% error reduction compared to baseline

Effective use of linguistic, semantic, and social features

Clustering captures narrative regularities like love-triangles

Abstract

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.