  Event extraction involves the detection and extraction of both the event
triggers and corresponding event arguments. Existing systems often decompose
event extraction into multiple subtasks, without considering their possible
interactions. In this paper, we propose EventGraph, a joint framework for event
extraction, which encodes events as graphs. We represent event triggers and
arguments as nodes in a semantic graph. Event extraction therefore becomes a
graph parsing problem, which provides the following advantages: 1) performing
event detection and argument extraction jointly; 2) detecting and extracting
multiple events from a piece of text; and 3) capturing the complicated
interaction between event arguments and triggers. Experimental results on
ACE2005 show that our model is competitive to state-of-the-art systems and has
substantially improved the results on argument extraction. Additionally, we
create two new datasets from ACE2005 where we keep the entire text spans for
event arguments, instead of just the head word(s). Our code and models are
released as open-source.
