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 …
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 …
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.
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