  In this work, we propose a novel segmental hypergraph representation to model
overlapping entity mentions that are prevalent in many practical datasets. We
show that our model built on top of such a new representation is able to
capture features and interactions that cannot be captured by previous models
while maintaining a low time complexity for inference. We also present a
theoretical analysis to formally assess how our representation is better than
alternative representations reported in the literature in terms of
representational power. Coupled with neural networks for feature learning, our
model achieves the state-of-the-art performance in three benchmark datasets
annotated with overlapping mentions.
