Modeling Dynamic Relationships Between Characters in Literary Novels
This addresses the challenge of computationally representing evolving character interactions in narratives, which is incremental as it builds on prior work focused on character roles.
The paper tackles the problem of modeling dynamic relationships between characters in literary novels, formulating it as a structured prediction task and proposing a semi-supervised framework with a Markovian model, which empirically outperforms competitive baselines.
Studying characters plays a vital role in computationally representing and interpreting narratives. Unlike previous work, which has focused on inferring character roles, we focus on the problem of modeling their relationships. Rather than assuming a fixed relationship for a character pair, we hypothesize that relationships are dynamic and temporally evolve with the progress of the narrative, and formulate the problem of relationship modeling as a structured prediction problem. We propose a semi-supervised framework to learn relationship sequences from fully as well as partially labeled data. We present a Markovian model capable of accumulating historical beliefs about the relationship and status changes. We use a set of rich linguistic and semantically motivated features that incorporate world knowledge to investigate the textual content of narrative. We empirically demonstrate that such a framework outperforms competitive baselines.