Grounding Characters and Places in Narrative Texts

TL;DR

This paper introduces a new task for categorizing spatial relationships between characters and locations in narrative texts, using annotated data and a contextual embedding model to analyze mobility and space occupation patterns.

Contribution

It proposes a novel spatial relationship categorization task, creates an annotated dataset, and develops a model to jointly analyze characters and places in stories.

Findings

Protagonists are more mobile than non-central characters.

Women tend to occupy more interior space than men.

The model effectively predicts spatial relationships in narrative texts.

Abstract

Tracking characters and locations throughout a story can help improve the understanding of its plot structure. Prior research has analyzed characters and locations from text independently without grounding characters to their locations in narrative time. Here, we address this gap by proposing a new spatial relationship categorization task. The objective of the task is to assign a spatial relationship category for every character and location co-mention within a window of text, taking into consideration linguistic context, narrative tense, and temporal scope. To this end, we annotate spatial relationships in approximately 2500 book excerpts and train a model using contextual embeddings as features to predict these relationships. When applied to a set of books, this model allows us to test several hypotheses on mobility and domestic space, revealing that protagonists are more mobile than non-central characters and that women as characters tend to occupy more interior space than men. Overall, our work is the first step towards joint modeling and analysis of characters and places in narrative text.