  We consider the problem of embedding character-entity relationships from the
reduced semantic space of narratives, proposing and evaluating the assumption
that these relationships hold under a reflection operation. We analyze this
assumption and compare the approach to a baseline state-of-the-art model with a
unique evaluation that simulates efficacy on a downstream clustering task with
human-created labels. Although our model creates clusters that achieve
Silhouette scores of -.084, outperforming the baseline -.227, our analysis
reveals that the models approach the task much differently and perform well on
very different examples. We conclude that our assumption might be useful for
specific types of data and should be evaluated on a wider range of tasks.
