Fine-grained entity typing aims to assign entity mentions in the free text
with types arranged in a hierarchical structure. Traditional distant
supervision based methods employ a structured data source as a weak supervision
and do not need hand-labeled data, but they neglect the label noise in the
automatically labeled training corpus. Although recent studies use many
features to prune wrong data ahead of training, they suffer from error
propagation and bring much complexity. In this paper, we propose an end-to-end
typing model, called the path-based attention neural model (PAN), to learn a
noise- robust performance by leveraging the hierarchical structure of types.
Experiments demonstrate its effectiveness.Comment: AAAI 201
