An attentive neural architecture for fine-grained entity type classification

S Shimaoka, P Stenetorp, K Inui, S Riedel - arXiv preprint arXiv …, 2016 - arxiv.org
S Shimaoka, P Stenetorp, K Inui, S Riedel
arXiv preprint arXiv:1604.05525, 2016arxiv.org
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 …
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.
arxiv.org