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.

本文提出了一种新颖的基于注意力机制的神经网络模型，用于精细分类实体类型，与以前的模型不同的是，我们的模型递归地构建实体提及上下文的表示。我们的模型在著名的 FIGER 数据集上实现了 74.94% 的松散微平均 F1 分数，相对提高了 2.59％。我们还研究了我们的模型中注意机制的行为，并观察到它可以学习表明实体精细类别成员资格的上下文语言表达式。