  We present a system for rapidly customizing event extraction capability to
find new event types and their arguments. The system allows a user to find,
expand and filter event triggers for a new event type by exploring an
unannotated corpus. The system will then automatically generate mention-level
event annotation automatically, and train a Neural Network model for finding
the corresponding event. Additionally, the system uses the ACE corpus to train
an argument model for extracting Actor, Place, and Time arguments for any event
types, including ones not seen in its training data. Experiments show that with
less than 10 minutes of human effort per event type, the system achieves good
performance for 67 novel event types. The code, documentation, and a
demonstration video will be released as open source on github.com.
