Extracting Entities and Events as a Single Task Using a Transition-Based Neural
Model
Abstract
The task of event extraction contains subtasks including detections for entity mentions, event triggers and argument roles. Traditional methods solve
them as a pipeline, which does not make use of task
correlation for their mutual benefits. There have
been recent efforts towards building a joint model
for all tasks. However, due to technical challenges,
there has not been work predicting the joint output
structure as a single task. We build a first model to
this end using a neural transition-based framework,
incrementally predicting complex joint structures
in a state-transition process. Results on standard
benchmarks show the benefits of the joint model,
which gives the best result in the literature