  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.
