  Fine-grained entity typing is the task of assigning fine-grained semantic
types to entity mentions. We propose a neural architecture which learns a
distributional semantic representation that leverages a greater amount of
semantic context -- both document and sentence level information -- than prior
work. We find that additional context improves performance, with further
improvements gained by utilizing adaptive classification thresholds.
Experiments show that our approach without reliance on hand-crafted features
achieves the state-of-the-art results on three benchmark datasets.
