We propose a new approach to the task of fine grained entity type classifications
based on label embeddings that allows for information sharing among related labels.
Specifically, we learn an embedding for each label and each feature such that
labels which frequently co-occur are close in the embedded space. We show that it
outperforms state-of-the-art methods on two fine grained entity-classification
benchmarks and that the model can exploit the finer-grained labels to improve
classification of standard coarse types.