  Human engagement in narrative is partially driven by reasoning about
discourse relations between narrative events, and the expectations about what
is likely to happen next that results from such reasoning. Researchers in NLP
have tackled modeling such expectations from a range of perspectives, including
treating it as the inference of the contingent discourse relation, or as a type
of common-sense causal reasoning. Our approach is to model likelihood between
events by drawing on several of these lines of previous work. We implement and
evaluate different unsupervised methods for learning event pairs that are
likely to be contingent on one another. We refine event pairs that we learn
from a corpus of film scene descriptions utilizing web search counts, and
evaluate our results by collecting human judgments of contingency. Our results
indicate that the use of web search counts increases the average accuracy of
our best method to 85.64% over a baseline of 50%, as compared to an average
accuracy of 75.15% without web search.
