Shimaoka, S; Stenetorp, P; Inui, K; Riedel, S; (2016) An Attentive Neural Architecture for Fine-grained Entity Type Classification. In: Proceedings of the 5th Workshop on Automated Knowledge Base Construction (AKBC) at the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Proceedings of the Workshop. (pp. pp. 69-74). Association for Computational Linguistics (ACL): San Diego, CA, USA.

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Abstract

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 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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