"You're Mr. Lebowski, I'm the Dude": Inducing Address Term Formality in Signed Social Networks

TL;DR

This paper introduces an unsupervised model that infers signed social networks from dialogue content, identifying relationship signs, content distributions, and triadic feature weights, applied to address term formality in movies.

Contribution

The paper presents a novel unsupervised approach for modeling signed social networks and analyzing address term formality, with a new bootstrapping technique for address term identification.

Findings

Coherent clustering of address terms achieved

Accurate judgments of social relation formality in films

Effective unsupervised inference of signed networks

Abstract

We present an unsupervised model for inducing signed social networks from the content exchanged across network edges. Inference in this model solves three problems simultaneously: (1) identifying the sign of each edge; (2) characterizing the distribution over content for each edge type; (3) estimating weights for triadic features that map to theoretical models such as structural balance. We apply this model to the problem of inducing the social function of address terms, such as 'Madame', 'comrade', and 'dude'. On a dataset of movie scripts, our system obtains a coherent clustering of address terms, while at the same time making intuitively plausible judgments of the formality of social relations in each film. As an additional contribution, we provide a bootstrapping technique for identifying and tagging address terms in dialogue.