  Much of the user-generated content on social media is provided by ordinary
people telling stories about their daily lives. We develop and test a novel
method for learning fine-grained common-sense knowledge from these stories
about contingent (causal and conditional) relationships between everyday
events. This type of knowledge is useful for text and story understanding,
information extraction, question answering, and text summarization. We test and
compare different methods for learning contingency relation, and compare what
is learned from topic-sorted story collections vs. general-domain stories. Our
experiments show that using topic-specific datasets enables learning
finer-grained knowledge about events and results in significant improvement
over the baselines. An evaluation on Amazon Mechanical Turk shows 82% of the
relations between events that we learn from topic-sorted stories are judged as
contingent.
