Existing event extraction work often heavily relies on a rich set of hand-designed features and utilizes existing NLP toolkits and resources (Ji and Grishman, 2008;Patwardhan and Riloff, 2009;Liao and Grishman, 2010;McClosky et al., 2011;Huang and Riloff, 2012;Li et al., 2013a;Li et al., 2013b;Li et al., 2014).
despite this limitation, FBRNN achieved competitive result on Rich ERE with only 0.8% difference from the best reported system in the TAC 2015 competition.