  Event extraction is a classic task in natural language processing with wide
use in handling large amount of yet rapidly growing financial, legal, medical,
and government documents which often contain multiple events with their
elements scattered and mixed across the documents, making the problem much more
difficult. Though the underlying relations between event elements to be
extracted provide helpful contextual information, they are somehow overlooked
in prior studies. We showcase the enhancement to this task brought by utilizing
the knowledge graph that captures entity relations and their attributes. We
propose a first event extraction framework that embeds a knowledge graph
through a Graph Neural Network and integrates the embedding with regular
features, all at document-level. Specifically, for extracting events from
Chinese financial announcements, our method outperforms the state-of-the-art
method by 5.3% in F1-score.
