An Attentive Neural Architecture for Fine-grained Entity Type Classification


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 linguistic expressions that indicate the fine-grained category memberships of an entity.

Extracted Key Phrases

4 Figures and Tables

Citations per Year

Citation Velocity: 8

Averaging 8 citations per year over the last 2 years.

Learn more about how we calculate this metric in our FAQ.

Cite this paper

@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} }