An Attentive Neural Architecture for Fine-grained Entity Type Classification
2016
In this work we propose a novel attentionbased
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-theart
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.
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