  The previous work for event extraction has mainly focused on the predictions
for event triggers and argument roles, treating entity mentions as being
provided by human annotators. This is unrealistic as entity mentions are
usually predicted by some existing toolkits whose errors might be propagated to
the event trigger and argument role recognition. Few of the recent work has
addressed this problem by jointly predicting entity mentions, event triggers
and arguments. However, such work is limited to using discrete engineering
features to represent contextual information for the individual tasks and their
interactions. In this work, we propose a novel model to jointly perform
predictions for entity mentions, event triggers and arguments based on the
shared hidden representations from deep learning. The experiments demonstrate
the benefits of the proposed method, leading to the state-of-the-art
performance for event extraction.
