  In this work we propose a novel attention-based neural network model for the
task of fine-grained entity type classification that unlike previously proposed
models recursively composes representations of entity mention contexts. Our
model achieves state-of-the-art performance with 74.94% loose micro F1-score on
the well-established FIGER dataset, a relative improvement of 2.59%. We also
investigate the behavior of the attention mechanism of our model and observe
that it can learn contextual linguistic expressions that indicate the
fine-grained category memberships of an entity.
