10358@AAAI

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#1 Modeling Evolving Relationships Between Characters in Literary Novels [PDF] [Copy] [Kimi] [REL]

Authors: Snigdha Chaturvedi, Shashank Srivastava, Hal Daume III, Chris Dyer

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.

Subject: AAAI.2016 - NLP and Machine Learning