An Attentive Neural Architecture for Fine-grained Entity Type Classification

WS 2016 Sonse ShimaokaPontus StenetorpKentaro InuiSebastian Riedel

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%... (read more)

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