  We tackle the nested and overlapping event detection task and propose a novel
search-based neural network (SBNN) structured prediction model that treats the
task as a search problem on a relation graph of trigger-argument structures.
Unlike existing structured prediction tasks such as dependency parsing, the
task targets to detect DAG structures, which constitute events, from the
relation graph. We define actions to construct events and use all the beams in
a beam search to detect all event structures that may be overlapping and
nested. The search process constructs events in a bottom-up manner while
modelling the global properties for nested and overlapping structures
simultaneously using neural networks. We show that the model achieves
performance comparable to the state-of-the-art model Turku Event Extraction
System (TEES) on the BioNLP Cancer Genetics (CG) Shared Task 2013 without the
use of any syntactic and hand-engineered features. Further analyses on the
development set show that our model is more computationally efficient while
yielding higher F1-score performance.
