Modeling Evolving Relationships Between Characters in Literary Novels

Authors

  • Snigdha Chaturvedi University of Maryland, College Park
  • Shashank Srivastava Carnegie Mellon University
  • Hal Daume III University of Maryland, College Park
  • Chris Dyer Carnegie Mellon University

DOI:

https://doi.org/10.1609/aaai.v30i1.10358

Keywords:

Relationship Modeling, Dynamic Relationships, Structured Prediction, Semi-supervised Methods, Latent Variable Models

Abstract

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 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.

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Published

2016-03-05

How to Cite

Chaturvedi, S., Srivastava, S., Daume III, H., & Dyer, C. (2016). Modeling Evolving Relationships Between Characters in Literary Novels. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10358

Issue

Section

Technical Papers: NLP and Machine Learning