  Inspired by the double temporality characteristic of narrative texts, we
propose a novel approach for acquiring rich temporal "before/after" event
knowledge across sentences in narrative stories. The double temporality states
that a narrative story often describes a sequence of events following the
chronological order and therefore, the temporal order of events matches with
their textual order. We explored narratology principles and built a weakly
supervised approach that identifies 287k narrative paragraphs from three large
text corpora. We then extracted rich temporal event knowledge from these
narrative paragraphs. Such event knowledge is shown useful to improve temporal
relation classification and outperform several recent neural network models on
the narrative cloze task.
