  Fine-grained entity typing is a challenging problem since it usually involves
a relatively large tag set and may require to understand the context of the
entity mention. In this paper, we use entity linking to help with the
fine-grained entity type classification process. We propose a deep neural model
that makes predictions based on both the context and the information obtained
from entity linking results. Experimental results on two commonly used datasets
demonstrates the effectiveness of our approach. On both datasets, it achieves
more than 5\% absolute strict accuracy improvement over the state of the art.
