Modeling Dynamic Relationships Between Characters in Literary Novels
Table 2: Cross validation performances of various models. The second order model based framework outperforms the one that uses a first order model and the unstructured baselines LR and J48.
['[BOLD] Model', '[BOLD] P', '[BOLD] R', '[BOLD] F', '[BOLD] ED']
Since the performance of the semi-supervised frameworks depends on random initialization of the weights, the figures reported in the table are mean values over 100 random restarts. The number of relationship states, |Y|, was set to be 2 to correspond to the gold standard annotations. From the table we can see that the framework with the first order Markov model yields slightly better performance (higher averaged F-measure and lower mean Edit Distance) than the unstructured models (LR and J48). This hints at a need for modeling the information flow between sentences of the sequences. The further performance improvement with the second order model emphasizes this hypothesis and also demonstrates the benefit of remembering longer history of characters while making relationship judgments.