  Comprehending an article requires understanding its constituent events.
However, the context where an event is mentioned often lacks the details of
this event. A question arises: how can the reader obtain more knowledge about
this particular event in addition to what is provided by the local context in
the article?
  This work defines Event Linking, a new natural language understanding task at
the event level. Event linking tries to link an event mention appearing in an
article to the most appropriate Wikipedia page. This page is expected to
provide rich knowledge about what the event mention refers to. To standardize
the research in this new direction, we contribute in four-fold. First, this is
the first work in the community that formally defines Event Linking task.
Second, we collect a dataset for this new task. Specifically, we automatically
gather training set from Wikipedia, and then create two evaluation sets: one
from the Wikipedia domain, reporting the in-domain performance, and a second
from the real-world news domain, to evaluate out-of-domain performance. Third,
we retrain and evaluate two state-of-the-art (SOTA) entity linking models,
showing the challenges of event linking, and we propose an event-specific
linking system EVELINK to set a competitive result for the new task. Fourth, we
conduct a detailed and insightful analysis to help understand the task and the
limitation of the current model. Overall, as our analysis shows, Event Linking
is a considerably challenging and essential task requiring more effort from the
community. Data and code are available here:
https://github.com/CogComp/event-linking.
