Abstract
Traditional approaches to the task of ACE
event extraction usually depend on manually
annotated data, which is often laborious to create and limited in size. Therefore, in addition to the difficulty of event extraction itself,
insufficient training data hinders the learning
process as well. To promote event extraction,
we first propose an event extraction model to
overcome the roles overlap problem by separating the argument prediction in terms of
roles. Moreover, to address the problem of insufficient training data, we propose a method
to automatically generate labeled data by editing prototypes and screen out generated samples by ranking the quality. Experiments on
the ACE2005 dataset demonstrate that our extraction model can surpass most existing extraction methods. Besides, incorporating our
generation method exhibits further significant
improvement. It obtains new state-of-the-art
results on the event extraction task, including
pushing the F1 score of trigger classification to
81.1%, and the F1 score of argument classifi-
cation to 58.9%.