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An Attentive Neural Architecture for Fine-grained Entity Type Classification
2016
Proceedings of the 5th Workshop on Automated Knowledge Base Construction
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 F1score 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
doi:10.18653/v1/w16-1313
dblp:conf/akbc/ShimaokaSIR16
fatcat:qt6nloof2fdhlfm7txwmwvqgoe