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arXiv · 1604.05525

An Attentive Neural Architecture for Fine-grained Entity Type Classification

Sonse Shimaoka·Pontus Stenetorp·Kentaro Inui·Sebastian Riedel
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Abstract

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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Sonse Shimaoka, Pontus Stenetorp, Kentaro Inui, Sebastian Riedel. 2016-04-19. An Attentive Neural Architecture for Fine-grained Entity Type Classification. https://arxiv.org/abs/1604.05525

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