# Modeling Dynamic Relationships Between Characters in Literary Novels

arXiv: https://arxiv.org/abs/1511.09376 · HTML: https://mlsanity.com/paper/1511.09376/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2015.

## Built on by (most cited first, 4 of 4)

- [Synopses of Movie Narratives: a Video-Language Dataset for Story Understanding](https://mlsanity.com/paper/2203.05711/) (2022)
- [Multilingual Synopses of Movie Narratives: A Dataset for Vision-Language Story Understanding](https://mlsanity.com/paper/2406.13092/) (2024)
- [MARCUS: An Event-Centric NLP Pipeline that generates Character Arcs from Narratives](https://mlsanity.com/paper/2510.18201/) (2025)
- [Think Before you Write: QA-Guided Reasoning for Character Descriptions in Books](https://mlsanity.com/paper/2604.11435/) (2026)

## Abstract (arXiv)

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.