An Attentive Neural Architecture for Fine grained Entity Type Classification
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.
AKBC@NAACL HLT