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.
@article{arxiv.1604.05525,
title = {An Attentive Neural Architecture for Fine-grained Entity Type Classification},
author = {Sonse Shimaoka and Pontus Stenetorp and Kentaro Inui and Sebastian Riedel},
journal= {arXiv preprint arXiv:1604.05525},
year = {2016}
}