An Attentive Neural Architecture for Fine-grained Entity Type Classification

@inproceedings{Shimaoka2016AnAN,
  title={An Attentive Neural Architecture for Fine-grained Entity Type Classification},
  author={Sonse Shimaoka and Pontus Stenetorp and Kentaro Inui and Sebastian Riedel},
  booktitle={AKBC@NAACL-HLT},
  year={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 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… CONTINUE READING

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  • 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% .

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