  We propose to leverage news discourse profiling to model document-level
temporal structures for building temporal dependency graphs. Our key
observation is that the functional roles of sentences used for profiling news
discourse signify different time frames relevant to a news story and can,
therefore, help to recover the global temporal structure of a document. Our
analyses and experiments with the widely used knowledge distillation technique
show that discourse profiling effectively identifies distant inter-sentence
event and (or) time expression pairs that are temporally related and otherwise
difficult to locate.
