Improving Temporal Relation Extraction with a Globally Acquired
  Statistical Resource
  Extracting temporal relations (before, after, overlapping, etc.) is a key
aspect of understanding events described in natural language. We argue that
this task would gain from the availability of a resource that provides prior
knowledge in the form of the temporal order that events usually follow. This
paper develops such a resource -- a probabilistic knowledge base acquired in
the news domain -- by extracting temporal relations between events from the New
York Times (NYT) articles over a 20-year span (1987--2007). We show that
existing temporal extraction systems can be improved via this resource. As a
byproduct, we also show that interesting statistics can be retrieved from this
resource, which can potentially benefit other time-aware tasks. The proposed
system and resource are both publicly available.
